Ming-Yang Yu

PINNForge: Execution-Grounded Evolutionary Design of Physics-Informed Neural Networks for PDE Solving via Large Language Models

PINNMorph: Evolving Online Adaptation Policies for Physics-Informed Neural Networks

Physics-informed neural networks (PINNs) provide a learning-based framework for solving partial differential equations (PDEs), yet their training behavior can change substantially throughout optimization. Residual distributions, gradient interactions, regional learning difficulty, and model-capacity requirements may ev...

PINNForge: Execution-Grounded Evolutionary Design of Physics-Informed Neural Networks

PINNForge is developed, an execution-grounded large language model (LLM)-driven evolutionary framework that treats observed PINN training behavior as a cross-generation design signal and achieves the lowest mean MSE on 24 tasks among RoPINN, PINNsFormer, PINNsAgent, and PINNForge.

Evolutionary Algorithm-Guided LLMs for Physics-Informed Neural Network Design

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 to demonstrate the feasibility of evolutionary-algorithm-guided LLMs for PINN design on a controlled PDE while motiva...