
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.





