3 papers
cs.AI2026
PINNMorph: Evolving Online Adaptation Policies for Physics-Informed Neural Networks
Xu Yang, Mingyang Yu, Jun Zhang +2
Physics-informed neural networks (PINNs) provide a learning-based framework for solving partial differential equations (PDEs), yet their training behavior can change substantially…
cs.AI2026
PINNForge: Execution-Grounded Evolutionary Design of Physics-Informed Neural Networks
Mingyang Yu, Xu Yang, Jun Zhang +3
Physics-informed neural networks (PINNs) require coordinated choices across representation, architecture, sampling, constraints, loss construction, and optimization, yet effective…
cs.NE2026
Evolutionary Algorithm-Guided LLMs for Physics-Informed Neural Network Design
Xu Yang, Mingyang Yu, Jing Xu +1
Physics-informed neural networks (PINNs) are unusually sensitive to interacting choices of architecture, activation, loss weighting, collocation, optimization, and constraint enfor…