Add conditional DENIM v2 protocol

Add strict role assignment, training code, metrics and validation for using all 2,660 trajectories without duplicating data files.

CONDITIONAL_V2_SHA256SUMS

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README.md

@@ -44,6 +44,15 @@ Start with `DENIM_START_HERE.md` for the incomplete-physics study,
 `T2_V2_START_HERE.md` for the multiaxial benchmark, or `START_HERE.md` for the
 original v1 release.
 
+
+## Material-conditioned DENIM v2 protocol
+
+All 2,660 trajectories now have a frozen role in the conditional study: 1,484
+train, 407 validation and 769 test. The release adds no duplicate data. Shared
+known-material pretraining plus incomplete-material replay reduces long-history
+RMSE from 10.906 to 4.263 MPa. Reproduction code, exact accounting and complete
+metrics are in `conditional_v2/`.
+
 ## DENIM incomplete-physics closure
 
 ![DENIM closure summary](artifacts/t2_graybox_closure_v1/closure_summary.png)

conditional_v2/README.md

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+# Conditional DENIM v2 protocol
+
+This release adds no duplicate trajectory files. It supplies the frozen 2,660-trajectory role assignment, training code, metrics and validation for material-conditioned DENIM v2. See `docs/T2_CONDITIONAL_DENIM_V2.md`.

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conditional_v2/artifacts/t2_denim_conditional_v2/physical_runtime_validation.json

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+            "memory_increment": 9.902433930619736e-07,
+            "isotropic": 6.159833901620004e-06
+          },
+          {
+            "memory": 2.6031077293708904e-09,
+            "memory_increment": 1.0412430810902151e-06,
+            "isotropic": 6.197422408149578e-06
+          }
+        ]
+      }
+    ]
+  },
+  "evaluation": {
+    "hidden_frozen_test": {
+      "trajectory_count": 32,
+      "rmse_mpa": 0.7980589270591736,
+      "mae_mpa": 0.39469894766807556,
+      "r2": 0.9999515414237976,
+      "state_point_count": 7712
+    },
+    "test_path_ood": {
+      "trajectory_count": 80,
+      "rmse_mpa": 0.782439112663269,
+      "mae_mpa": 0.39284950494766235,
+      "r2": 0.9999520778656006,
+      "state_point_count": 19280
+    },
+    "test_amplitude_ood": {
+      "trajectory_count": 60,
+      "rmse_mpa": 2.67646861076355,
+      "mae_mpa": 0.9395037889480591,
+      "r2": 0.9994901418685913,
+      "state_point_count": 14460
+    },
+    "test_long_horizon": {
+      "trajectory_count": 40,
+      "rmse_mpa": 4.262782096862793,
+      "mae_mpa": 1.5307230949401855,
+      "r2": 0.9986092448234558,
+      "state_point_count": 9640
+    },
+    "test_discretization": {
+      "trajectory_count": 20,
+      "rmse_mpa": 0.9083492159843445,
+      "mae_mpa": 0.4616833031177521,
+      "r2": 0.9999374747276306,
+      "state_point_count": 8120
+    },
+    "known_strict_test_all": {
+      "trajectory_count": 537,
+      "rmse_mpa": 3.3084354400634766,
+      "mae_mpa": 1.3567044734954834,
+      "r2": 0.9991792440414429,
+      "state_point_count": 115017
+    }
+  }
+}

conditional_v2/docs/T2_CONDITIONAL_DENIM_V2.md

@@ -0,0 +1,78 @@
+# Material-conditioned DENIM v2
+
+## 结果摘要
+
+DENIM v2 将已有 2,660 条材料加载轨迹纳入同一套严格分层协议。数据没有被无差别
+混合：1,484 条用于训练，407 条用于验证，769 条保持为测试。训练集包括 1,154 条
+J2/Chaboche 已知材料轨迹和 330 条不完备材料轨迹；其余轨迹承担材料、路径、参数、
+幅值、长历史和时间离散边界测试。
+
+模型保留弹性、J2 屈服几何、关联流动、塑性不可压缩和隐式返回映射。神经网络读取
+无量纲材料描述符，学习共享的各向同性硬化、运动硬化模量和恢复演化。对于不完备
+材料，描述符只含 `E`、`nu`、初始屈服应力和材料族标识，参考三记忆参数、硬化表和
+演化公式均未输入模型。
+
+## 数据使用
+
+| 数据层 | 总数 | 训练 | 验证 | 测试用途 |
+|---|---:|---:|---:|---|
+| 基础材料记忆 v1 | 1,008 | 768 | 120 | 120 条同协议测试 |
+| 多轴 OOD v2 | 1,024 | 386 | 221 | 417 条严格联合留出 |
+| DENIM 闭合与边界 | 628 | 330 | 66 | 232 条路径、幅值、长历史和步长边界 |
+| **合计** | **2,660** | **1,484** | **407** | **769** |
+
+多轴 v2 只有在 ID、路径 OOD 和参数 OOD 三套划分中同时属于训练的轨迹才进入训练。
+任何一套协议中的测试轨迹都不会参与优化。
+
+## 模型结果
+
+| 不完备材料测试 | DENIM v1.1 专用模型 | 条件架构、仅 330 条训练 | **DENIM v2 全数据** |
+|---|---:|---:|---:|
+| 冻结未知路径 RMSE | **0.714 MPa** | 0.565 MPa | 0.798 MPa |
+| 新 Path-OOD RMSE | **0.743 MPa** | 0.612 MPa | 0.782 MPa |
+| 幅值 OOD RMSE | **2.294 MPa** | 1.542 MPa | 2.676 MPa |
+| 长历史 RMSE | 10.906 MPa | 10.566 MPa | **4.263 MPa** |
+
+条件架构本身在短路径上具有较高数据效率。加入 2,032 条已知材料数据后，最终模型
+在普通路径上保留约 0.8 MPa 的精度，同时把最主要的长历史误差降低约 61%。这是一组
+清楚的 Pareto 结果：专用模型适合固定材料的最高短程精度，条件模型适合跨材料使用和
+更稳定的长历史演化。
+
+最终条件模型在 537 条已知材料严格测试轨迹上的 RMSE 为 **3.308 MPa**，`R2` 为
+**0.999179**。模型有 8,477 个参数。24 条代表轨迹的物理审计结果为：最大屈服残差
+192 Pa、PEEQ 最小增量为 0、塑性应变最大迹 `2.33e-9`，全部输出有限。
+
+## 科研价值
+
+1. 给出了一套可复现的跨协议数据利用方法：旧数据用于共享材料演化表征，新数据用于
+   不完备物理闭合，全部留出集保持冻结。
+2. 证明已知材料中的硬化与恢复经验可以迁移为不完备材料的长期演化先验。长历史改善
+   与短路径精度权衡同时公开，为研究负迁移、灾难性遗忘和多尺度材料记忆提供了证据。
+3. 模型仍维持显式力学骨架和可解释内部状态，研究问题从单材料曲线拟合扩展到材料条件
+   化的神经本构族。
+4. 数据覆盖完整物理上限、不完备物理闭合和跨材料迁移三层协议，适合继续研究材料描述
+   符、少样本标定、元学习、长期状态稳定性及结构求解中的切线一致性。
+
+## 工程价值
+
+1. 同一模型文件可处理参数化 J2、Chaboche 和不完备合成材料，降低为每种材料维护独立
+   网络的成本。
+2. 材料参数通过明确的 13 维描述符输入，具备形成材料库、批量标定和多区域调用接口的
+   基础。
+3. 长历史误差显著下降，对循环加载、路径依赖和长期状态滚动更有意义。
+4. 屈服面、关联流动、PEEQ 单调性和塑性不可压缩由积分结构保持，适合继续接入隐式有限
+   元求解。
+
+当前 v2 完成的是材料点条件化验证，尚未制作 AgentFEM 全局求解 Provider。CPU 审计吞吐
+约为 469 个状态点/秒，隐式神经本构明显慢于直接 GRU。下一步工程化重点是描述符缓存、
+材料点批处理、严格一致切线以及 AgentFEM-learning 的通用条件材料 Provider。
+
+## 复现入口
+
+- 模型核心：`src/t2_denim_conditional.py`
+- 全数据训练：`src/train_t2_denim_conditional_v2.py`
+- 隐藏材料重点精调：`src/refine_t2_denim_conditional_v2.py`
+- 物理与运行审计：`src/validate_t2_denim_conditional_v2.py`
+- 完整指标：`artifacts/t2_denim_conditional_v2/model_metrics.json`
+- 精调指标：`artifacts/t2_denim_conditional_v2/refined_metrics.json`
+

conditional_v2/src/infer_t2_denim_conditional_v2.py

@@ -0,0 +1,58 @@
+"""Minimal offline inference for the material-conditioned DENIM v2 bundle."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+import torch
+from safetensors.torch import load_file
+
+
+def run(bundle: Path) -> dict[str, float]:
+    repository_root = bundle.parent
+    if (repository_root / "src").is_dir():
+        sys.path.insert(0, str(repository_root))
+    from src.t2_denim_conditional import ConditionalDENIM, material_descriptor, rollout
+
+    model = ConditionalDENIM()
+    model.load_state_dict(load_file(bundle / "weights.safetensors"))
+    model.eval()
+    parameters = {
+        "material_model": "chaboche_combined",
+        "young_pa": 190.0e9,
+        "poisson": 0.30,
+        "yield_stress_pa": 280.0e6,
+        "backstress_c1_pa": 28.0e9,
+        "backstress_gamma1": 80.0,
+        "backstress_c2_pa": 10.0e9,
+        "backstress_gamma2": 12.0,
+        "isotropic_saturation_pa": 70.0e6,
+        "isotropic_rate": 8.0,
+    }
+    strain = torch.zeros((1, 121, 6))
+    strain[0, :, 0] = torch.linspace(0.0, 0.01, 121)
+    with torch.no_grad():
+        result = rollout(
+            strain,
+            torch.tensor([parameters["young_pa"]]),
+            torch.tensor([parameters["poisson"]]),
+            torch.tensor([parameters["yield_stress_pa"]]),
+            material_descriptor(parameters)[None],
+            model,
+        )
+    summary = {
+        "maximum_absolute_stress_mpa": float(result["stress"].abs().max() / 1.0e6),
+        "final_peeq": float(result["peeq"][0, -1]),
+        "maximum_yield_residual_pa": float(result["yield_residual"].abs().max()),
+    }
+    print(json.dumps(summary, indent=2))
+    return summary
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--bundle", type=Path, default=Path(__file__).resolve().parent)
+    run(parser.parse_args().bundle)

conditional_v2/src/refine_t2_denim_conditional_v2.py

@@ -0,0 +1,80 @@
+"""Hidden-family refinement with known-family replay for conditional DENIM v2."""
+
+from __future__ import annotations
+
+import copy
+import json
+from collections import defaultdict
+
+import torch
+
+from src.t2_denim_conditional import ConditionalDENIM
+from src.train_t2_denim_conditional_v2 import (
+    ARTIFACT_DIR,
+    MODEL_DIR,
+    _train_stage,
+    evaluate,
+    load_all_trajectories,
+    transitions,
+)
+
+
+def run(*, steps: int = 1200) -> dict[str, object]:
+    trajectories = load_all_trajectories()
+    hidden_name = "hidden_three_memory_tabulated_hardening"
+    known_train = [x for x in trajectories if x.material != hidden_name and x.role == "train"]
+    known_validation = [x for x in trajectories if x.material != hidden_name and x.role == "validation"]
+    hidden_train = [x for x in trajectories if x.material == hidden_name and x.role == "train"]
+    hidden_validation = [x for x in trajectories if x.material == hidden_name and x.role == "validation"]
+    data = {
+        "known_train": transitions(known_train),
+        "known_validation": transitions(known_validation),
+        "hidden_train": transitions(hidden_train),
+        "hidden_validation": transitions(hidden_validation),
+    }
+    checkpoint = torch.load(MODEL_DIR / "denim_conditional_v2.pt", map_location="cpu", weights_only=False)
+    model = ConditionalDENIM()
+    model.load_state_dict(checkpoint["state_dict"])
+    refined = copy.deepcopy(model)
+    training = _train_stage(
+        refined,
+        [data["known_train"], data["hidden_train"], data["hidden_train"], data["hidden_train"]],
+        [data["known_validation"], data["hidden_validation"], data["hidden_validation"], data["hidden_validation"]],
+        steps=steps,
+        seed=20260930,
+        learning_rate=5.0e-4,
+    )
+    groups: dict[str, list] = defaultdict(list)
+    for trajectory in trajectories:
+        groups[trajectory.evaluation].append(trajectory)
+    selected = (
+        "hidden_frozen_test",
+        "test_path_ood",
+        "test_amplitude_ood",
+        "test_long_horizon",
+        "test_discretization",
+    )
+    metrics: dict[str, object] = {
+        "training": training,
+        "evaluation": {name: evaluate(refined, groups[name]) for name in selected},
+    }
+    known_test = [x for x in trajectories if x.material != hidden_name and x.role == "test"]
+    metrics["evaluation"]["known_strict_test_all"] = evaluate(refined, known_test)
+    torch.save(
+        {
+            "state_dict": refined.state_dict(),
+            "architecture": "ConditionalDENIM(channels=2, embedding=32, hidden=48)",
+            "stage": "hidden-focused refinement with known-family replay",
+        },
+        MODEL_DIR / "denim_conditional_v2_refined.pt",
+    )
+    ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
+    (ARTIFACT_DIR / "refined_metrics.json").write_text(
+        json.dumps(metrics, indent=2) + "\n", encoding="utf-8"
+    )
+    print(json.dumps(metrics["evaluation"], indent=2))
+    return metrics
+
+
+if __name__ == "__main__":
+    run()

conditional_v2/src/t2_denim_conditional.py

@@ -0,0 +1,328 @@
+"""Material-conditioned DENIM constitutive core.
+
+The module retains the small-strain elastic/J2/associative-flow return map
+used by DENIM and conditions only the unknown hardening closure on a compact
+material descriptor.  It deliberately does not encode the closed-form J2 or
+Chaboche hardening equations.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from src.t2_graybox_discrete_energy import (
+    GrayboxState,
+    deviatoric,
+    double_contract,
+    elastic_stress,
+    initial_state,
+    von_mises,
+)
+
+
+DESCRIPTOR_NAMES = (
+    "young_scaled",
+    "poisson",
+    "yield_scaled",
+    "linear_isotropic_scaled",
+    "backstress_c1_scaled",
+    "backstress_gamma1_scaled",
+    "backstress_c2_scaled",
+    "backstress_gamma2_scaled",
+    "isotropic_saturation_scaled",
+    "isotropic_rate_scaled",
+    "family_j2",
+    "family_chaboche",
+    "family_incomplete",
+)
+
+
+def material_descriptor(parameters: dict[str, float | str]) -> torch.Tensor:
+    """Build a dimensionless descriptor without exposing hidden DENIM laws."""
+
+    family = str(parameters["material_model"])
+    indicators = {
+        "j2_linear_isotropic": (1.0, 0.0, 0.0),
+        "chaboche_combined": (0.0, 1.0, 0.0),
+        "hidden_three_memory_tabulated_hardening": (0.0, 0.0, 1.0),
+    }
+    if family not in indicators:
+        raise ValueError(f"Unsupported material family: {family}")
+    # The incomplete family exposes only E, nu and initial yield stress.  The
+    # zero entries are intentional, not missing-data imputation.
+    hidden = family == "hidden_three_memory_tabulated_hardening"
+    value = (
+        float(parameters["young_pa"]) / 200.0e9,
+        float(parameters["poisson"]),
+        float(parameters["yield_stress_pa"]) / 300.0e6,
+        0.0 if hidden else float(parameters.get("hardening_modulus_pa", 0.0)) / 5.0e9,
+        0.0 if hidden else float(parameters.get("backstress_c1_pa", 0.0)) / 30.0e9,
+        0.0 if hidden else float(parameters.get("backstress_gamma1", 0.0)) / 80.0,
+        0.0 if hidden else float(parameters.get("backstress_c2_pa", 0.0)) / 10.0e9,
+        0.0 if hidden else float(parameters.get("backstress_gamma2", 0.0)) / 20.0,
+        0.0 if hidden else float(parameters.get("isotropic_saturation_pa", 0.0)) / 100.0e6,
+        0.0 if hidden else float(parameters.get("isotropic_rate", 0.0)) / 10.0,
+        *indicators[family],
+    )
+    return torch.tensor(value, dtype=torch.float32)
+
+
+class MaterialEncoder(nn.Module):
+    def __init__(self, descriptor_size: int = len(DESCRIPTOR_NAMES), hidden: int = 32):
+        super().__init__()
+        self.network = nn.Sequential(
+            nn.Linear(descriptor_size, hidden),
+            nn.SiLU(),
+            nn.Linear(hidden, hidden),
+            nn.SiLU(),
+        )
+
+    def forward(self, descriptor: torch.Tensor) -> torch.Tensor:
+        return self.network(descriptor)
+
+
+class ConditionalIsotropicHardening(nn.Module):
+    """Descriptor-conditioned, zero-anchored monotone hardening curve."""
+
+    def __init__(self, embedding: int = 32, neurons: int = 12, plastic_scale: float = 0.01):
+        super().__init__()
+        self.neurons = int(neurons)
+        self.plastic_scale = float(plastic_scale)
+        self.parameter_head = nn.Linear(embedding, 2 * neurons + 1)
+
+    def _positive_parameters(
+        self, embedding: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        raw = self.parameter_head(embedding)
+        weight = 0.45 * torch.sigmoid(raw[..., : self.neurons])
+        slope = F.softplus(raw[..., self.neurons : 2 * self.neurons]) + 1.0e-5
+        linear = 0.60 * torch.sigmoid(raw[..., -1])
+        return weight, slope, linear
+
+    def forward(
+        self,
+        peeq: torch.Tensor,
+        stress_scale: torch.Tensor,
+        embedding: torch.Tensor,
+    ) -> torch.Tensor:
+        weight, slope, linear = self._positive_parameters(embedding)
+        coordinate = peeq[..., None] / self.plastic_scale
+        saturation = -torch.expm1(-slope * coordinate)
+        dimensionless = linear * coordinate.squeeze(-1) + (weight * saturation).sum(dim=-1)
+        return stress_scale * dimensionless
+
+
+class ConditionalDENIM(nn.Module):
+    """Shared neural hardening closure conditioned on material metadata."""
+
+    def __init__(self, channels: int = 2, embedding: int = 32, hidden: int = 48):
+        super().__init__()
+        self.channels = int(channels)
+        self.encoder = MaterialEncoder(hidden=embedding)
+        self.isotropic = ConditionalIsotropicHardening(embedding=embedding)
+        self.modulus_head = nn.Sequential(
+            nn.Linear(embedding, hidden), nn.SiLU(), nn.Linear(hidden, channels)
+        )
+        state_size = 3 + 3 * channels + embedding
+        self.recovery = nn.Sequential(
+            nn.Linear(state_size, hidden),
+            nn.SiLU(),
+            nn.Linear(hidden, hidden),
+            nn.SiLU(),
+            nn.Linear(hidden, channels),
+        )
+
+    def encode(self, descriptor: torch.Tensor) -> torch.Tensor:
+        return self.encoder(descriptor)
+
+    def moduli(self, stress_scale: torch.Tensor, embedding: torch.Tensor) -> torch.Tensor:
+        # 0--400 times the initial yield stress covers the synthetic families
+        # while retaining a bounded, nonnegative kinematic modulus.
+        return 400.0 * stress_scale[..., None] * torch.sigmoid(self.modulus_head(embedding))
+
+    def isotropic_radius(
+        self,
+        peeq: torch.Tensor,
+        stress_scale: torch.Tensor,
+        embedding: torch.Tensor,
+    ) -> torch.Tensor:
+        return self.isotropic(peeq, stress_scale, embedding)
+
+    def update_memories(
+        self,
+        state: GrayboxState,
+        flow_direction: torch.Tensor,
+        increment: torch.Tensor,
+        stress_scale: torch.Tensor,
+        embedding: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        radius = self.isotropic_radius(state.peeq, stress_scale, embedding)
+        old_norm = torch.sqrt(torch.clamp(double_contract(state.previous_flow, state.previous_flow), min=0.0))
+        current_norm = torch.sqrt(torch.clamp(double_contract(flow_direction, flow_direction), min=0.0))
+        reversal = double_contract(state.previous_flow, flow_direction) / (
+            old_norm * current_norm
+        ).clamp_min(1.0e-12)
+        scale = stress_scale.clamp_min(1.0)
+        norms = torch.sqrt(torch.clamp(double_contract(state.memories, state.memories), min=0.0)) / scale[..., None]
+        projections = double_contract(state.memories, flow_direction[..., None, :]) / scale[..., None]
+        cross = torch.zeros_like(norms)
+        if self.channels > 1:
+            for index in range(self.channels):
+                other = (index + 1) % self.channels
+                cross[..., index] = double_contract(
+                    state.memories[..., index, :], state.memories[..., other, :]
+                ) / scale.square()
+        scalars = torch.stack((state.peeq / 0.05, radius / scale, reversal), dim=-1)
+        features = torch.cat((scalars, norms, projections, cross, embedding), dim=-1)
+        recovery = 250.0 * torch.sigmoid(self.recovery(features)) + 1.0e-7
+        moduli = self.moduli(stress_scale, embedding)
+        numerator = state.memories + (
+            (2.0 / 3.0)
+            * moduli[..., :, None]
+            * increment[..., None, None]
+            * flow_direction[..., None, :]
+        )
+        denominator = 1.0 + recovery * increment[..., None]
+        return numerator / denominator[..., None], recovery, moduli
+
+
+def _candidate_update(
+    trial_deviatoric: torch.Tensor,
+    state: GrayboxState,
+    increment: torch.Tensor,
+    shear: torch.Tensor,
+    yield_stress: torch.Tensor,
+    embedding: torch.Tensor,
+    law: ConditionalDENIM,
+    *,
+    direction_iterations: int = 6,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    shifted = trial_deviatoric - state.backstress
+    direction = 1.5 * shifted / von_mises(shifted).clamp_min(1.0)[..., None]
+    memories = state.memories
+    recovery = moduli = None
+    for _ in range(direction_iterations):
+        memories, recovery, moduli = law.update_memories(
+            state, direction, increment, yield_stress, embedding
+        )
+        denominator = 1.0 + recovery * increment[..., None]
+        effective = trial_deviatoric - (
+            state.memories / denominator[..., None]
+        ).sum(dim=-2)
+        direction = 1.5 * effective / von_mises(effective).clamp_min(1.0)[..., None]
+    memories, recovery, moduli = law.update_memories(
+        state, direction, increment, yield_stress, embedding
+    )
+    denominator = 1.0 + recovery * increment[..., None]
+    effective = trial_deviatoric - (state.memories / denominator[..., None]).sum(dim=-2)
+    radius = yield_stress + law.isotropic_radius(
+        state.peeq + increment, yield_stress, embedding
+    )
+    effective_modulus = 3.0 * shear + (moduli / denominator).sum(dim=-1)
+    residual = von_mises(effective) - effective_modulus * increment - radius
+    return residual, direction, memories, recovery, moduli
+
+
+def advance(
+    strain: torch.Tensor,
+    state: GrayboxState,
+    young: torch.Tensor,
+    poisson: torch.Tensor,
+    yield_stress: torch.Tensor,
+    descriptor: torch.Tensor,
+    law: ConditionalDENIM,
+    *,
+    bisection_iterations: int = 24,
+    direction_iterations: int = 6,
+) -> tuple[torch.Tensor, GrayboxState, dict[str, torch.Tensor]]:
+    embedding = law.encode(descriptor)
+    trial = elastic_stress(strain, state.plastic_strain, young, poisson)
+    trial_deviatoric = deviatoric(trial)
+    shifted = trial_deviatoric - state.backstress
+    old_radius = yield_stress + law.isotropic_radius(state.peeq, yield_stress, embedding)
+    trial_function = von_mises(shifted) - old_radius
+    plastic = trial_function > yield_stress.clamp_min(1.0) * 1.0e-12
+    shear = young / (2.0 * (1.0 + poisson))
+    lower = torch.zeros_like(trial_function)
+    upper = 2.0 * F.relu(trial_function) / (3.0 * shear).clamp_min(1.0) + 1.0e-14
+    for _ in range(16):
+        residual, *_ = _candidate_update(
+            trial_deviatoric, state, upper, shear, yield_stress, embedding, law,
+            direction_iterations=direction_iterations,
+        )
+        upper = torch.where(plastic & (residual > 0.0), 2.0 * upper, upper)
+    for _ in range(bisection_iterations):
+        middle = 0.5 * (lower + upper)
+        residual, *_ = _candidate_update(
+            trial_deviatoric, state, middle, shear, yield_stress, embedding, law,
+            direction_iterations=direction_iterations,
+        )
+        lower = torch.where(plastic & (residual > 0.0), middle, lower)
+        upper = torch.where(plastic & (residual <= 0.0), middle, upper)
+    increment = torch.where(plastic, 0.5 * (lower + upper), torch.zeros_like(lower))
+    residual, direction, memories, recovery, moduli = _candidate_update(
+        trial_deviatoric, state, increment, shear, yield_stress, embedding, law,
+        direction_iterations=direction_iterations,
+    )
+    direction = torch.where(plastic[..., None], direction, torch.zeros_like(direction))
+    memories = torch.where(plastic[..., None, None], memories, state.memories)
+    updated = GrayboxState(
+        plastic_strain=state.plastic_strain + increment[..., None] * direction,
+        peeq=state.peeq + increment,
+        memories=memories,
+        previous_flow=torch.where(plastic[..., None], direction, state.previous_flow),
+    )
+    stress = elastic_stress(strain, updated.plastic_strain, young, poisson)
+    return stress, updated, {
+        "plastic_increment": increment,
+        "yield_residual": torch.where(plastic, residual, torch.zeros_like(residual)),
+        "recovery": recovery,
+        "moduli": moduli,
+        "plastic": plastic,
+    }
+
+
+def rollout(
+    strain: torch.Tensor,
+    young: torch.Tensor,
+    poisson: torch.Tensor,
+    yield_stress: torch.Tensor,
+    descriptor: torch.Tensor,
+    law: ConditionalDENIM,
+    *,
+    bisection_iterations: int = 24,
+) -> dict[str, torch.Tensor]:
+    batch, points, _ = strain.shape
+    state = initial_state(batch, channels=law.channels, dtype=strain.dtype, device=strain.device)
+    result: dict[str, list[torch.Tensor]] = {
+        name: []
+        for name in (
+            "stress", "plastic_strain", "peeq", "memories",
+            "plastic_increment", "yield_residual", "recovery", "moduli",
+        )
+    }
+    for point in range(points):
+        stress, state, diagnostics = advance(
+            strain[:, point], state, young, poisson, yield_stress, descriptor, law,
+            bisection_iterations=bisection_iterations,
+        )
+        result["stress"].append(stress)
+        result["plastic_strain"].append(state.plastic_strain)
+        result["peeq"].append(state.peeq)
+        result["memories"].append(state.memories)
+        for name in ("plastic_increment", "yield_residual", "recovery", "moduli"):
+            result[name].append(diagnostics[name])
+    return {name: torch.stack(values, dim=1) for name, values in result.items()}
+
+
+__all__ = [
+    "ConditionalDENIM",
+    "DESCRIPTOR_NAMES",
+    "advance",
+    "material_descriptor",
+    "rollout",
+]

conditional_v2/src/train_t2_denim_conditional_v2.py

@@ -0,0 +1,507 @@
+"""Train a material-conditioned DENIM with all 2,660 trajectories assigned.
+
+The 1,008 basic-memory and 1,024 multiaxial trajectories teach a shared
+conditional hardening representation.  The 628 incomplete-physics trajectories
+then train and evaluate the closure target.  Frozen holdouts remain holdouts;
+"use all data" means every trajectory has an explicit train/validation/test
+role, not that benchmark cases are leaked into optimization.
+"""
+
+from __future__ import annotations
+
+import argparse
+import copy
+import json
+import random
+import time
+from collections import Counter, defaultdict
+from dataclasses import dataclass
+from pathlib import Path
+
+import h5py
+import numpy as np
+import torch
+
+from src.t2_denim_conditional import ConditionalDENIM, material_descriptor, rollout
+
+
+ROOT = Path(__file__).resolve().parents[1]
+V1_ROOT = ROOT / "data" / "t2_material_loading_memory_v1" / "shards"
+V2_ROOT = ROOT / "data" / "t2_multiaxial_ood_v2" / "shards"
+CLOSURE_PATH = ROOT / "data" / "t2_graybox_closure_v1" / "cohort.h5"
+BOUNDARY_PATH = ROOT / "data" / "t2_denim_boundary_v1" / "cohort.h5"
+MODEL_DIR = ROOT / "models" / "t2_denim_conditional_v2"
+ARTIFACT_DIR = ROOT / "artifacts" / "t2_denim_conditional_v2"
+
+
+@dataclass
+class Trajectory:
+    strain: torch.Tensor
+    stress: torch.Tensor
+    plastic: torch.Tensor
+    peeq: torch.Tensor
+    increment: torch.Tensor
+    memories: torch.Tensor
+    radius: torch.Tensor
+    descriptor: torch.Tensor
+    young: float
+    poisson: float
+    yield_stress: float
+    source: str
+    family: str
+    material: str
+    role: str
+    evaluation: str
+
+
+@dataclass
+class Transitions:
+    increment: torch.Tensor
+    flow: torch.Tensor
+    previous_flow: torch.Tensor
+    old_peeq: torch.Tensor
+    new_peeq: torch.Tensor
+    old_memories: torch.Tensor
+    new_memories: torch.Tensor
+    new_radius: torch.Tensor
+    descriptor: torch.Tensor
+    yield_stress: torch.Tensor
+
+    def __len__(self) -> int:
+        return len(self.increment)
+
+    def select(self, indices: torch.Tensor) -> "Transitions":
+        return Transitions(**{name: value[indices] for name, value in vars(self).items()})
+
+
+def _voigt(value: np.ndarray) -> np.ndarray:
+    return value[..., (0, 1, 2, 0, 1, 0), (0, 1, 2, 1, 2, 2)]
+
+
+def _parameters(group: h5py.Group) -> dict[str, float | str]:
+    return json.loads(str(group.attrs["parameters_json"]))
+
+
+def _standard_trajectory(
+    group: h5py.Group,
+    *,
+    source: str,
+    role: str,
+    evaluation: str,
+) -> Trajectory:
+    parameters = _parameters(group)
+    material = str(parameters["material_model"])
+    if "total_strain_voigt" in group:
+        strain = group["total_strain_voigt"][:]
+        stress = group["stress_voigt_pa"][:]
+        plastic = group["plastic_strain_voigt"][:]
+    else:
+        strain = _voigt(group["total_strain"][:])
+        stress = _voigt(group["stress_pa"][:])
+        plastic = _voigt(group["plastic_strain"][:])
+    if material == "j2_linear_isotropic":
+        memories = np.zeros((len(strain), 2, 6), dtype=np.float64)
+    else:
+        memories = _voigt(group["backstress_components_pa"][:])
+    yield_stress = float(parameters["yield_stress_pa"])
+    radius = group["yield_radius_pa"][:] - yield_stress
+    return Trajectory(
+        strain=torch.tensor(strain, dtype=torch.float32),
+        stress=torch.tensor(stress, dtype=torch.float32),
+        plastic=torch.tensor(plastic, dtype=torch.float32),
+        peeq=torch.tensor(group["equivalent_plastic_strain"][:], dtype=torch.float32),
+        increment=torch.tensor(group["plastic_multiplier_increment"][:], dtype=torch.float32),
+        memories=torch.tensor(memories, dtype=torch.float32),
+        radius=torch.tensor(radius, dtype=torch.float32),
+        descriptor=material_descriptor(parameters),
+        young=float(parameters["young_pa"]),
+        poisson=float(parameters["poisson"]),
+        yield_stress=yield_stress,
+        source=source,
+        family=str(group.attrs["path_family"]),
+        material=material,
+        role=role,
+        evaluation=evaluation,
+    )
+
+
+def _hidden_trajectory(group: h5py.Group, *, source: str) -> Trajectory:
+    parameters = _parameters(group)
+    split = str(group.attrs["split"])
+    role = "train" if split == "train" else "validation" if split == "validation" else "test"
+    if split == "test":
+        evaluation = "hidden_frozen_test"
+    elif split.startswith("test_"):
+        evaluation = split
+        role = "test"
+    else:
+        evaluation = f"hidden_{role}"
+    return Trajectory(
+        strain=torch.tensor(group["strain"][:], dtype=torch.float32),
+        stress=torch.tensor(group["stress_pa"][:], dtype=torch.float32),
+        plastic=torch.tensor(group["plastic_strain"][:], dtype=torch.float32),
+        peeq=torch.tensor(group["peeq"][:], dtype=torch.float32),
+        increment=torch.tensor(group["plastic_increment"][:], dtype=torch.float32),
+        memories=torch.tensor(group["memories_pa"][:], dtype=torch.float32),
+        radius=torch.tensor(group["isotropic_radius_pa"][:], dtype=torch.float32),
+        descriptor=material_descriptor(parameters),
+        young=float(parameters["young_pa"]),
+        poisson=float(parameters["poisson"]),
+        yield_stress=float(parameters["yield_stress_pa"]),
+        source=source,
+        family=str(group.attrs["path_family"]),
+        material=str(parameters["material_model"]),
+        role=role,
+        evaluation=evaluation,
+    )
+
+
+def load_all_trajectories() -> list[Trajectory]:
+    trajectories: list[Trajectory] = []
+    for path in sorted(V1_ROOT.glob("*.h5")):
+        with h5py.File(path, "r") as h5:
+            for name in sorted(h5):
+                group = h5[name]
+                split = str(group.attrs["split"])
+                trajectories.append(
+                    _standard_trajectory(
+                        group, source="basic_memory_v1", role=split,
+                        evaluation=f"v1_{split}",
+                    )
+                )
+    for path in sorted(V2_ROOT.glob("*.h5")):
+        with h5py.File(path, "r") as h5:
+            for name in sorted(h5):
+                group = h5[name]
+                splits = tuple(
+                    str(group.attrs[field])
+                    for field in ("split_id", "split_path_ood", "split_parameter_ood")
+                )
+                if all(value == "train" for value in splits):
+                    role = "train"
+                elif any(value == "test" for value in splits):
+                    role = "test"
+                else:
+                    role = "validation"
+                labels = []
+                for protocol, value in zip(("id", "path_ood", "parameter_ood"), splits, strict=True):
+                    if value == "test":
+                        labels.append(protocol)
+                evaluation = "v2_" + ("+".join(labels) if labels else role)
+                trajectories.append(
+                    _standard_trajectory(
+                        group, source="multiaxial_ood_v2", role=role,
+                        evaluation=evaluation,
+                    )
+                )
+    with h5py.File(CLOSURE_PATH, "r") as h5:
+        for name in sorted(h5):
+            trajectories.append(_hidden_trajectory(h5[name], source="denim_closure_v1"))
+    with h5py.File(BOUNDARY_PATH, "r") as h5:
+        for name in sorted(h5):
+            trajectories.append(_hidden_trajectory(h5[name], source="denim_boundary_v1"))
+    if len(trajectories) != 2660:
+        raise RuntimeError(f"Expected 2,660 trajectories, found {len(trajectories)}")
+    return trajectories
+
+
+def transitions(trajectories: list[Trajectory]) -> Transitions:
+    values: dict[str, list[torch.Tensor]] = defaultdict(list)
+    for trajectory in trajectories:
+        increment = trajectory.increment[1:]
+        active = increment > 1.0e-11
+        if not torch.any(active):
+            continue
+        old_plastic = trajectory.plastic[:-1]
+        new_plastic = trajectory.plastic[1:]
+        flow = (new_plastic - old_plastic) / increment.clamp_min(1.0e-14)[:, None]
+        previous_increment = trajectory.increment[:-1]
+        previous_plastic = torch.cat((torch.zeros_like(old_plastic[:1]), old_plastic[:-1]), dim=0)
+        previous_flow = (old_plastic - previous_plastic) / previous_increment.clamp_min(1.0e-14)[:, None]
+        previous_flow = torch.where(
+            (previous_increment > 1.0e-11)[:, None], previous_flow,
+            torch.zeros_like(previous_flow),
+        )
+        count = int(active.sum())
+        values["increment"].append(increment[active])
+        values["flow"].append(flow[active])
+        values["previous_flow"].append(previous_flow[active])
+        values["old_peeq"].append(trajectory.peeq[:-1][active])
+        values["new_peeq"].append(trajectory.peeq[1:][active])
+        values["old_memories"].append(trajectory.memories[:-1][active])
+        values["new_memories"].append(trajectory.memories[1:][active])
+        values["new_radius"].append(trajectory.radius[1:][active])
+        values["descriptor"].append(trajectory.descriptor[None].expand(count, -1))
+        values["yield_stress"].append(torch.full((count,), trajectory.yield_stress))
+    return Transitions(**{name: torch.cat(parts, dim=0) for name, parts in values.items()})
+
+
+def _loss(model: ConditionalDENIM, data: Transitions) -> tuple[torch.Tensor, dict[str, float]]:
+    from src.t2_graybox_discrete_energy import GrayboxState
+
+    count = len(data)
+    state = GrayboxState(
+        plastic_strain=torch.zeros((count, 6)),
+        peeq=data.old_peeq,
+        memories=data.old_memories,
+        previous_flow=data.previous_flow,
+    )
+    embedding = model.encode(data.descriptor)
+    predicted, _, _ = model.update_memories(
+        state, data.flow, data.increment, data.yield_stress, embedding
+    )
+    radius = model.isotropic_radius(data.new_peeq, data.yield_stress, embedding)
+    scale = data.yield_stress.clamp_min(1.0)
+    memory = ((predicted - data.new_memories) / scale[:, None, None]).square().mean()
+    delta = (
+        ((predicted - data.old_memories) - (data.new_memories - data.old_memories))
+        / (0.05 * scale[:, None, None])
+    ).square().mean()
+    isotropic = ((radius - data.new_radius) / scale).square().mean()
+    total = memory + 0.08 * delta + isotropic
+    return total, {
+        "memory": float(memory.detach()),
+        "memory_increment": float(delta.detach()),
+        "isotropic": float(isotropic.detach()),
+    }
+
+
+def _train_stage(
+    model: ConditionalDENIM,
+    train_sets: list[Transitions],
+    validation_sets: list[Transitions],
+    *,
+    steps: int,
+    seed: int,
+    batch_size: int = 4096,
+    learning_rate: float = 2.0e-3,
+) -> dict[str, object]:
+    optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=1.0e-6)
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, steps, eta_min=2.0e-5)
+    generator = torch.Generator().manual_seed(seed)
+    best = float("inf")
+    best_state = None
+    history = []
+    started = time.perf_counter()
+    for step in range(steps):
+        optimizer.zero_grad()
+        total = torch.zeros(())
+        diagnostics = []
+        for data in train_sets:
+            index = torch.randint(len(data), (min(batch_size, len(data)),), generator=generator)
+            value, detail = _loss(model, data.select(index))
+            total = total + value / len(train_sets)
+            diagnostics.append(detail)
+        total.backward()
+        torch.nn.utils.clip_grad_norm_(model.parameters(), 10.0)
+        optimizer.step()
+        scheduler.step()
+        if step % 50 == 0 or step + 1 == steps:
+            model.eval()
+            with torch.no_grad():
+                validation = []
+                for data in validation_sets:
+                    # Deterministic cap keeps validation fast and balanced.
+                    selected = torch.arange(min(24000, len(data)))
+                    validation.append(float(_loss(model, data.select(selected))[0]))
+                score = sum(validation) / len(validation)
+            model.train()
+            history.append(
+                {
+                    "step": step,
+                    "training_loss": float(total.detach()),
+                    "validation_loss": score,
+                    "parts": diagnostics,
+                }
+            )
+            if score < best:
+                best = score
+                best_state = copy.deepcopy(model.state_dict())
+    if best_state is None:
+        raise RuntimeError("Training stage produced no checkpoint")
+    model.load_state_dict(best_state)
+    model.eval()
+    return {
+        "steps": steps,
+        "seconds": time.perf_counter() - started,
+        "best_validation_loss": best,
+        "history": history,
+    }
+
+
+def _metric(prediction: torch.Tensor, reference: torch.Tensor) -> dict[str, float | int]:
+    error = prediction - reference
+    denominator = (reference - reference.mean()).square().sum().clamp_min(1.0)
+    return {
+        "trajectory_count": len(reference),
+        "rmse_mpa": float(torch.sqrt(error.square().mean()) / 1.0e6),
+        "mae_mpa": float(error.abs().mean() / 1.0e6),
+        "r2": float(1.0 - error.square().sum() / denominator),
+    }
+
+
+def evaluate(model: ConditionalDENIM, selected: list[Trajectory]) -> dict[str, float | int]:
+    predictions = []
+    references = []
+    by_points: dict[int, list[Trajectory]] = defaultdict(list)
+    for item in selected:
+        by_points[len(item.strain)].append(item)
+    with torch.no_grad():
+        for group in by_points.values():
+            for start in range(0, len(group), 96):
+                batch = group[start : start + 96]
+                strain = torch.stack([item.strain for item in batch])
+                result = rollout(
+                    strain,
+                    torch.tensor([item.young for item in batch]),
+                    torch.tensor([item.poisson for item in batch]),
+                    torch.tensor([item.yield_stress for item in batch]),
+                    torch.stack([item.descriptor for item in batch]),
+                    model,
+                    bisection_iterations=24,
+                )
+                predictions.append(result["stress"].reshape(-1, 6))
+                references.append(
+                    torch.stack([item.stress for item in batch]).reshape(-1, 6)
+                )
+    result = _metric(torch.cat(predictions), torch.cat(references))
+    result["trajectory_count"] = len(selected)
+    result["state_point_count"] = sum(len(item.strain) for item in selected)
+    return result
+
+
+def run(*, pretrain_steps: int = 1800, joint_steps: int = 1800, scratch_steps: int = 1800) -> dict[str, object]:
+    random.seed(20260927)
+    np.random.seed(20260927)
+    torch.manual_seed(20260927)
+    trajectories = load_all_trajectories()
+    known_train = [x for x in trajectories if x.material != "hidden_three_memory_tabulated_hardening" and x.role == "train"]
+    known_validation = [x for x in trajectories if x.material != "hidden_three_memory_tabulated_hardening" and x.role == "validation"]
+    hidden_train = [x for x in trajectories if x.material == "hidden_three_memory_tabulated_hardening" and x.role == "train"]
+    hidden_validation = [x for x in trajectories if x.material == "hidden_three_memory_tabulated_hardening" and x.role == "validation"]
+
+    datasets = {
+        "known_train": transitions(known_train),
+        "known_validation": transitions(known_validation),
+        "hidden_train": transitions(hidden_train),
+        "hidden_validation": transitions(hidden_validation),
+    }
+    pretrained = ConditionalDENIM()
+    pretraining = _train_stage(
+        pretrained, [datasets["known_train"]], [datasets["known_validation"]],
+        steps=pretrain_steps, seed=20260927,
+    )
+    print(json.dumps({"stage": "known_pretraining", "best_validation_loss": pretraining["best_validation_loss"]}), flush=True)
+    transfer = copy.deepcopy(pretrained)
+    joint_training = _train_stage(
+        transfer,
+        [datasets["known_train"], datasets["hidden_train"]],
+        [datasets["known_validation"], datasets["hidden_validation"]],
+        steps=joint_steps, seed=20260928,
+    )
+    print(json.dumps({"stage": "mixed_replay", "best_validation_loss": joint_training["best_validation_loss"]}), flush=True)
+    scratch = ConditionalDENIM()
+    scratch_training = _train_stage(
+        scratch, [datasets["hidden_train"]], [datasets["hidden_validation"]],
+        steps=scratch_steps, seed=20260929,
+    )
+    print(json.dumps({"stage": "hidden_scratch", "best_validation_loss": scratch_training["best_validation_loss"]}), flush=True)
+
+    MODEL_DIR.mkdir(parents=True, exist_ok=True)
+    torch.save({"state_dict": transfer.state_dict(), "stage": "pre_evaluation"}, MODEL_DIR / "denim_conditional_v2.inprogress.pt")
+
+    groups: dict[str, list[Trajectory]] = defaultdict(list)
+    for trajectory in trajectories:
+        groups[trajectory.evaluation].append(trajectory)
+    primary = (
+        "v1_test", "v2_id", "v2_path_ood", "v2_parameter_ood",
+        "v2_id+path_ood", "v2_id+parameter_ood", "v2_path_ood+parameter_ood",
+        "v2_id+path_ood+parameter_ood", "hidden_frozen_test",
+        "test_path_ood", "test_amplitude_ood", "test_long_horizon",
+        "test_discretization",
+    )
+    evaluations: dict[str, object] = {}
+    for name in primary:
+        if not groups[name]:
+            continue
+        evaluations[name] = {
+            "transfer": evaluate(transfer, groups[name]),
+            "scratch": evaluate(scratch, groups[name])
+            if groups[name][0].material == "hidden_three_memory_tabulated_hardening"
+            else None,
+        }
+    # Aggregate strict holdouts gives a stable headline for the two known families.
+    known_test = [x for x in trajectories if x.material != "hidden_three_memory_tabulated_hardening" and x.role == "test"]
+    hidden_test = [x for x in trajectories if x.material == "hidden_three_memory_tabulated_hardening" and x.role == "test" and len(x.strain) == 241]
+    evaluations["known_strict_test_all"] = {"transfer": evaluate(transfer, known_test)}
+    evaluations["hidden_all_fixed_length_test"] = {
+        "transfer": evaluate(transfer, hidden_test),
+        "scratch": evaluate(scratch, hidden_test),
+    }
+
+    accounting = {
+        "total": len(trajectories),
+        "by_source": dict(Counter(x.source for x in trajectories)),
+        "by_role": dict(Counter(x.role for x in trajectories)),
+        "training": len(known_train) + len(hidden_train),
+        "validation": len(known_validation) + len(hidden_validation),
+        "test": sum(x.role == "test" for x in trajectories),
+        "known_training": len(known_train),
+        "hidden_training": len(hidden_train),
+        "transition_counts": {name: len(data) for name, data in datasets.items()},
+    }
+    metrics: dict[str, object] = {
+        "name": "material-conditioned DENIM v2",
+        "scope": "three synthetic material families; shared conditional closure with strict frozen holdouts",
+        "parameter_count": sum(parameter.numel() for parameter in transfer.parameters()),
+        "accounting": accounting,
+        "training": {
+            "known_family_pretraining": pretraining,
+            "mixed_replay_transfer": joint_training,
+            "hidden_only_scratch": scratch_training,
+        },
+        "evaluation": evaluations,
+        "descriptor_policy": {
+            "known_families": "E, nu, initial yield plus declared hardening parameters and family indicator",
+            "incomplete_family": "E, nu, initial yield and family indicator only; hidden hardening parameters remain zero",
+        },
+    }
+    ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
+    torch.save(
+        {
+            "state_dict": transfer.state_dict(),
+            "architecture": "ConditionalDENIM(channels=2, embedding=32, hidden=48)",
+            "data_accounting": accounting,
+        },
+        MODEL_DIR / "denim_conditional_v2.pt",
+    )
+    torch.save(
+        {"state_dict": pretrained.state_dict(), "stage": "known_family_pretraining"},
+        MODEL_DIR / "denim_conditional_pretrained.pt",
+    )
+    torch.save(
+        {"state_dict": scratch.state_dict(), "stage": "hidden_only_scratch"},
+        MODEL_DIR / "denim_conditional_scratch.pt",
+    )
+    (ARTIFACT_DIR / "model_metrics.json").write_text(
+        json.dumps(metrics, indent=2) + "\n", encoding="utf-8"
+    )
+    print(json.dumps({"accounting": accounting, "evaluation": evaluations}, indent=2))
+    return metrics
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--pretrain-steps", type=int, default=1800)
+    parser.add_argument("--joint-steps", type=int, default=1800)
+    parser.add_argument("--scratch-steps", type=int, default=1800)
+    arguments = parser.parse_args()
+    run(
+        pretrain_steps=arguments.pretrain_steps,
+        joint_steps=arguments.joint_steps,
+        scratch_steps=arguments.scratch_steps,
+    )
+
+
+if __name__ == "__main__":
+    main()

conditional_v2/src/validate_t2_denim_conditional_v2.py

@@ -0,0 +1,80 @@
+"""Physical and runtime audit for the refined conditional DENIM v2."""
+
+from __future__ import annotations
+
+import json
+import time
+from collections import defaultdict
+
+import torch
+
+from src.t2_denim_conditional import ConditionalDENIM, rollout
+from src.train_t2_denim_conditional_v2 import ARTIFACT_DIR, MODEL_DIR, load_all_trajectories
+
+
+def run() -> dict[str, object]:
+    checkpoint = torch.load(
+        MODEL_DIR / "denim_conditional_v2_refined.pt",
+        map_location="cpu",
+        weights_only=False,
+    )
+    model = ConditionalDENIM()
+    model.load_state_dict(checkpoint["state_dict"])
+    model.eval()
+    trajectories = load_all_trajectories()
+    groups: dict[str, list] = defaultdict(list)
+    for trajectory in trajectories:
+        groups[trajectory.evaluation].append(trajectory)
+    selected = (
+        groups["hidden_frozen_test"][:8]
+        + groups["test_path_ood"][:8]
+        + groups["test_long_horizon"][:8]
+    )
+    strain = torch.stack([item.strain for item in selected])
+    started = time.perf_counter()
+    with torch.no_grad():
+        result = rollout(
+            strain,
+            torch.tensor([item.young for item in selected]),
+            torch.tensor([item.poisson for item in selected]),
+            torch.tensor([item.yield_stress for item in selected]),
+            torch.stack([item.descriptor for item in selected]),
+            model,
+            bisection_iterations=24,
+        )
+    seconds = time.perf_counter() - started
+    reference = torch.stack([item.stress for item in selected])
+    error = result["stress"] - reference
+    active = result["plastic_increment"] > 1.0e-11
+    report = {
+        "model": "material-conditioned DENIM v2 refined",
+        "parameter_count": sum(parameter.numel() for parameter in model.parameters()),
+        "audit_trajectory_count": len(selected),
+        "audit_state_point_count": int(strain.shape[0] * strain.shape[1]),
+        "stress_rmse_mpa": float(torch.sqrt(error.square().mean()) / 1.0e6),
+        "maximum_yield_residual_pa": float(result["yield_residual"][active].abs().max()),
+        "minimum_peeq_increment": float(torch.diff(result["peeq"], dim=1).min()),
+        "maximum_plastic_strain_trace": float(
+            result["plastic_strain"][..., :3].sum(dim=-1).abs().max()
+        ),
+        "maximum_memory_trace_pa": float(
+            result["memories"][..., :3].sum(dim=-1).abs().max()
+        ),
+        "all_finite": all(torch.isfinite(value).all() for value in result.values()),
+        "inference_seconds": seconds,
+        "state_points_per_second": float(strain.shape[0] * strain.shape[1] / seconds),
+        "descriptor_hidden_hardening_entries_zero": bool(
+            torch.count_nonzero(torch.stack([x.descriptor[3:10] for x in selected])) == 0
+        ),
+        "scope": "material-point audit; conditional v2 is not yet an AgentFEM global provider bundle",
+    }
+    ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
+    (ARTIFACT_DIR / "physical_runtime_validation.json").write_text(
+        json.dumps(report, indent=2) + "\n", encoding="utf-8"
+    )
+    print(json.dumps(report, indent=2))
+    return report
+
+
+if __name__ == "__main__":
+    run()

conditional_v2/tests/test_t2_denim_conditional.py

@@ -0,0 +1,68 @@
+from __future__ import annotations
+
+import torch
+
+from src.t2_denim_conditional import ConditionalDENIM, material_descriptor, rollout
+
+
+def test_descriptor_hides_incomplete_parameters() -> None:
+    descriptor = material_descriptor(
+        {
+            "material_model": "hidden_three_memory_tabulated_hardening",
+            "young_pa": 190.0e9,
+            "poisson": 0.3,
+            "yield_stress_pa": 280.0e6,
+            "backstress_c1_pa": 99.0e9,
+        }
+    )
+    assert descriptor.shape == (13,)
+    assert torch.count_nonzero(descriptor[3:10]) == 0
+    assert torch.equal(descriptor[-3:], torch.tensor([0.0, 0.0, 1.0]))
+
+
+def test_conditional_rollout_preserves_hard_constraints() -> None:
+    torch.manual_seed(7)
+    model = ConditionalDENIM()
+    strain = torch.zeros((2, 17, 6), dtype=torch.float32)
+    strain[:, :, 0] = torch.linspace(0.0, 0.006, 17)
+    descriptor = torch.stack(
+        [
+            material_descriptor(
+                {
+                    "material_model": "j2_linear_isotropic",
+                    "young_pa": 200.0e9,
+                    "poisson": 0.3,
+                    "yield_stress_pa": 300.0e6,
+                    "hardening_modulus_pa": 2.0e9,
+                }
+            ),
+            material_descriptor(
+                {
+                    "material_model": "chaboche_combined",
+                    "young_pa": 190.0e9,
+                    "poisson": 0.29,
+                    "yield_stress_pa": 280.0e6,
+                    "backstress_c1_pa": 25.0e9,
+                    "backstress_gamma1": 70.0,
+                    "backstress_c2_pa": 8.0e9,
+                    "backstress_gamma2": 12.0,
+                    "isotropic_saturation_pa": 60.0e6,
+                    "isotropic_rate": 8.0,
+                }
+            ),
+        ]
+    )
+    result = rollout(
+        strain,
+        torch.tensor([200.0e9, 190.0e9]),
+        torch.tensor([0.3, 0.29]),
+        torch.tensor([300.0e6, 280.0e6]),
+        descriptor,
+        model,
+        bisection_iterations=8,
+    )
+    assert torch.isfinite(result["stress"]).all()
+    assert torch.diff(result["peeq"], dim=1).min() >= 0.0
+    assert result["plastic_strain"][..., :3].sum(dim=-1).abs().max() < 1.0e-6
+    active = result["plastic_increment"] > 0.0
+    assert result["yield_residual"][active].abs().max() < 5.0e6