Evolutionary Algorithm-Guided LLMs Enable Automated Design of Physics-Informed Neural Networks
A new approach combines a closed-loop evolutionary algorithm with a large language model (LLM) to automate the design of physics-informed neural networks (PINNs). The system iteratively generates and evaluates complete PINN configurations, using training outcomes to inform subsequent generations. In experiments on a 1D multiscale wave equation, the best configuration emerged in the final generation, achieving up to a 95.38% reduction in mean-squared error compared to the initial population. The results demonstrate the feasibility of this method for automated PINN design.
Why it matters: Automating PINN design could accelerate progress in scientific computing by reducing the manual effort required to optimize neural network architectures for complex physical systems.
Full story at: arXiv AI/ML ↗