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cs.LG2025
PINN Balls: Scaling Second-Order Methods for PINNs with Domain Decomposition and Adaptive Sampling
Andrea Bonfanti, Ismael Medina, Roman List +3
Recent advances in Scientific Machine Learning have shown that second-order methods can enhance the training of Physics-Informed Neural Networks (PINNs), making them a suitable alt…
cs.LG2024★ 2 cited
The Challenges of the Nonlinear Regime for Physics-Informed Neural Networks
Andrea Bonfanti, Giuseppe Bruno, Cristina Cipriani
The Neural Tangent Kernel (NTK) viewpoint is widely employed to analyze the training dynamics of overparameterized Physics-Informed Neural Networks (PINNs). However, unlike the cas…