Integrating 3D Heat Equation into a PINN for Real-Time Aerospace Simulation (C++ WASM Engine)[P]

Reddit r/MachineLearning Tools
physics-informed-neural-networks pinn aerospace-simulation heat-equation webassembly cpp open-source real-time-simulation thermal-analysis re-entry

Summary

Met-Shield is an open-source re-entry simulator that uses a Physics-Informed Neural Network (PINN) to predict thermal gradients on a spacecraft shield, integrated into a C++ WebAssembly engine for real-time browser performance. The project addresses robustness over traditional solvers but faces convergence issues during high heat flux phases.

Hey everyone, I’ve been exploring **Physics-Informed Neural Networks (PINNs)** to solve high-velocity thermal problems. I built **Met-Shield**, a re-entry simulator that uses a PINN to predict thermal gradients on a spacecraft shield. **The PINN Phase:** * **Architecture:** I’m using a fully connected network trained to satisfy the **3D Heat Equation** as its primary loss function. * **Physics Constraints:** The model is constrained by the thermal diffusivity and conductivity of **Ti-6Al-4V (Titanium alloy)**. * **The Goal:** I wanted to see if a PINN could provide more robust generalization than a standard FDM solver when dealing with noisy atmospheric trajectory data. **The Performance Handoff:** Once trained, I integrated the model logic into a custom **C++ engine** compiled to **WebAssembly**. This allows the simulation to run natively in the browser at 60fps, predicting metallurgical phase transitions (Alpha-to-Beta Titanium) on the fly. **The Struggle:** While the PINN's math is solid, I’m seeing some convergence issues when the heat flux spikes during the "Max Q" phase of re-entry. I’m also looking for advice on better ways to weight the physics-loss vs. the data-loss in the total loss function. I’ve open-sourced the repo and would love for some ML engineers to look at my training loop and architecture. https://preview.redd.it/enkuqo7vg11h1.png?width=1920&format=png&auto=webp&s=7c69248a43e9c0488015ebbad1c78d6079c43e5f https://preview.redd.it/auh9uq6wg11h1.png?width=1920&format=png&auto=webp&s=5cb270a224012c68f33d0897fbd66742bb7a5152 **Repo:**[https://github.com/Lak23James/met-shield]() **Live Site:**[https://met-shield-58n1.vercel.app/]()
Original Article

Similar Articles

Physics-Informed Neural Networks with Learnable Loss Balancing and Transfer Learning

arXiv cs.LG

This paper proposes a self-supervised physics-informed neural network (PINN) framework with a learnable blending neuron to adaptively balance physics-based and data-driven losses, and integrates transfer learning to improve efficiency under data scarcity. It is validated on liquid-metal miniature heat sink CFD data with only 87 datapoints, achieving under 8% error.

Physics-based Digital Twins for Integrated Thermal Energy Systems Using Active Learning

arXiv cs.LG

This paper proposes an active learning framework to couple high-fidelity Modelica simulations with simpler surrogate models (SINDyC, FNN, GRU) for creating efficient digital twins of thermal energy distribution systems. The approach significantly reduces the number of simulation trajectories needed while maintaining predictive accuracy and enabling uncertainty quantification.