Update README and model configuration

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: pytorch
+tags:
+- OneScience
+- computational-fluid-dynamics
+- physics-informed-neural-network
+- navier-stokes
+- hard-boundary-conditions
+---
+<p align="center">
+  <strong>
+    <span style="font-size: 30px;">HardBCPINN-SinePipeFlow</span>
+  </strong>
+</p>
+
+# Model Overview
+
+HardBCPINN-SinePipeFlow is a reproduction of a hard-boundary-condition physics-informed neural network (PINN) for two-dimensional incompressible steady flow in a pipe with sinusoidal walls.
+
+Paper: [Solving Navier-Stokes Equations Using Data-free Physics-Informed Neural Networks With Hard Boundary Conditions](https://arxiv.org/pdf/2511.14497)
+
+# Model Description
+
+HardBCPINN-SinePipeFlow is trained without labeled flow-field data. It takes two-dimensional coordinates `(x, y)` as input and predicts velocity and pressure `(u, v, p)`. The implementation embeds the inlet pressure, outlet pressure, and no-slip wall conditions in an output transformation, so the model satisfies these hard boundary conditions throughout training. The interior flow is optimized using residuals of the incompressible Navier-Stokes equations.
+
+## Use Cases
+
+| Use Case | Description |
+| :--- | :--- |
+| Internal pipe-flow solution | Solve two-dimensional steady velocity and pressure fields in a pipe with sinusoidal boundaries |
+| PINN workflow validation | Validate data-free PINN training with hard boundary constraints |
+
+# Usage
+
+## 1. OneCode
+
+Use the online OneCode environment for an intelligent, one-click AI for Science (AI4S) programming experience:
+
+[Launch OneCode for one-click AI4S programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)
+
+## 2. Manual Setup
+
+**Hardware Requirements**
+
+- A GPU or DCU is recommended.
+- A CPU can be used for import checks and small-scale pipeline validation, but full training will be slow.
+- DCU users must install DTK in advance. DTK 25.04.2 or later, or the OneScience-recommended version for the target cluster, is recommended.
+
+### Download the Model Package from Hugging Face
+
+```bash
+pip install -U huggingface_hub
+hf download OneScience-Group/HardBCPINN-SinePipeFlow --local-dir ./HardBCPINN-SinePipeFlow
+cd HardBCPINN-SinePipeFlow
+```
+
+### Set Up the Runtime Environment
+
+**DCU Environment**
+
+```bash
+# Activate DTK and Conda first
+conda create -n onescience311 python=3.11 -y
+conda activate onescience311
+# Installation with uv is also supported
+pip install onescience[cfd-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
+```
+
+**GPU Environment**
+
+```bash
+# Activate Conda first
+conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12
+conda activate onescience311
+# Installation with uv is also supported
+pip install onescience[cfd-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
+```
+
+### Training Data
+
+This package reproduces the Reynolds-number-100 sinusoidal-wall pipe-flow experiment from Section 3.3 of the paper. It is a data-free PINN task and does not require an external labeled dataset. The script generates collocation points, boundary points, and a visualization grid from the geometry and boundary conditions specified in the paper:
+
+```bash
+python scripts/fake_data.py
+```
+
+The default geometry parameters are `R0=0.05`, `A=0.005`, `N=6`, and `L=1.0`. The inlet pressure is `0.1`, and the outlet pressure is `0.0`.
+
+### Training
+
+```bash
+python scripts/train.py
+```
+
+The default training configuration saves the checkpoint to:
+
+```text
+./weight/best_model.pth
+```
+
+### Model Weights
+
+The `weight/` directory contains a trained checkpoint that can be used directly for inference.
+
+- Weight file: `weight/best_model.pth`
+- File size: 340,962 bytes
+- SHA256: `f41bf976775dc18af7d01857dc858517c1978de9830c55afd68ccbf9b6cbd030`
+- Training length: 300,000 iterations
+- Measured metrics: `eval_loss_total=2.567469e-4`, `wall_u_max_abs=0`, and `wall_v_max_abs=0`
+
+Table 7 of the paper reports errors relative to the CFD reference solution at Re=100: `u=0.0016`, `v=0.0811`, and `p=0.0007`. This model package does not include the CFD reference field used by the paper, so it does not recompute relative L2 error; it reports the PDE residual and hard-boundary errors instead.
+
+### Inference
+
+```bash
+python scripts/inference.py
+```
+
+### Evaluation and Visualization
+
+```bash
+python scripts/result.py
+```
+
+# Official OneScience Resources
+
+| Platform | OneScience Repository | Skills Repository |
+| --- | --- | --- |
+| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
+| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
+
+# Citations and License
+
+- Original paper: [Solving Navier-Stokes Equations Using Data-free Physics-Informed Neural Networks With Hard Boundary Conditions](https://arxiv.org/pdf/2511.14497).
+- This repository retains source attribution and is organized for reproducible execution with OneScience and distribution through Hugging Face.