Evolutionary Algorithm-Guided LLMs for Physics-Informed Neural Network Design
Physics-informed neural networks (PINNs) are unusually sensitive to interacting choices of architecture, activation, loss weighting, collocation, optimization, and constraint enforcement. Large language models (LLMs) can propose these choices, but independent recommendations do not accumulate experience from previously trained PINNs. We propose a closed-loop evolutionary algorithm that guides an LLM to generate complete, executable PINN configurations across generations, using measured training outcomes to determine subsequent search decisions. The algorithm maintains an evaluated population and lineage, applies parent-conditioned mutation and crossover, preserves elite and diverse soluti...
| Authors | Xu Yang, Mingyang Yu, Jing Xu et al. |
|---|---|
| Primary category | cs.NE |
| Published | 17 July 2026 |
| Last updated at source | 17 July 2026 |
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