ThermoPINN：融合物理知识的神经网络实现实时热预测

ThermoPINN is an open-source project integrating Physics-Informed Neural Networks (PINN) with engineering thermodynamics to provide real-time thermal prediction services via FastAPI. It achieves a 3.5x inference speedup while maintaining high accuracy, addressing the real-time needs of modern engineering thermal management scenarios.

Modern engineering systems (EV batteries, data centers, aerospace) require precise and fast thermal prediction, but traditional numerical solvers (FEM, CFD) are too slow for real-time applications. PINN, which encodes physical laws into neural network loss functions, emerged as a solution—reducing data dependency, improving generalization, and enabling fast inference after training.

System flow: Numerical thermal solver → Ground truth temperature field → PINN training → Model inference → FastAPI endpoint → Render cloud deployment. Core components:

1. 2D steady-state thermal equation solver (generates ground truth data)
2. PINN model (input: spatial coordinates (x,y), output: temperature; loss includes data fitting and physical constraint (heat conduction PDE))
3. FastAPI service (RESTful API with Swagger UI, async support)
4. Cloud deployment on Render (low user threshold, production-ready)

Key metrics:

Metric	Value
MAE	0.18°C
RMSE	0.26°C
Max Error	2.32°C (boundary area)
Numerical Solver Time	~0.13s
PINN Inference Time	~0.036s
Speedup	~3.5x
ThermoPINN balances sub-degree accuracy and 3.5x faster inference than numerical solvers.

API endpoints:

• GET /health: Check service status
• POST /predict-pinn: Single-point temperature prediction
• POST /predict-batch: Batch prediction for multiple points
• POST /predict-grid: Full thermal field grid prediction
• POST /compare: Compare PINN vs numerical solver results Application scenarios: Battery thermal management (EV/energy storage), simulation acceleration (design optimization), digital twin (real-time state sync), electronic cooling design (smartphones, HPC chips)

Current limitations: Based on simplified 2D steady-state thermal equations with idealized geometry/boundary conditions. Future roadmap:

• Extend to 3D thermal modeling
• Support transient thermal equations (time dimension)
• Integrate with CAD models
• Specialized modeling for EV battery packs
• GPU inference acceleration
• Add MLOps practices (CI/CD, model versioning)

ThermoPINN provides an excellent reference for applying PINN in engineering thermal management. It bridges AI and engineering by combining physical knowledge with ML, demonstrating practical value in real-time scenarios. As an open-source project with complete documentation, it supports community learning and extension. Future evolutions will enhance its utility as an engineering tool.