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0ef4ba2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 | """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",
]
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