Search

Few direct matches — filled in with the latest updates.

Tag: #pde-solving2 results

LAM-PINN Boosts PINNs Against Task Heterogeneity

LAM-PINN Boosts PINNs Against Task Heterogeneity

LAM-PINN introduces compositional meta-learning to address task heterogeneity in physics-informed neural networks (PINNs) for parameterized PDEs. It clusters tasks using PDE parameters and learning-affinity metrics from brief transfers, decomposing the model into specialized subnetworks with learned routing. Achieves 19.7-fold MSE reduction on unseen tasks using only 10% of conventional PINN training iterations.

NMIPS: Neuro-Symbolic PDE Solver

NMIPS: Neuro-Symbolic PDE Solver

NMIPS introduces a unified neuro-symbolic framework for solving PDE families with shared structures but varying parameters. It discovers interpretable analytical solutions via multifactorial optimization and affine transfer for efficiency. Experiments show up to 35.7% accuracy gains over baselines.

ArXiv AIResearchFeb 13#research#arxiv#nmips