Finite basis kolmogorov-arnold networks: domain decomposition for data-driven and physics-informed problems
Kolmogorov-Arnold networks (KANs) have attracted attention recently as an alternative to
multilayer perceptrons (MLPs) for scientific machine learning. However, KANs can be …
multilayer perceptrons (MLPs) for scientific machine learning. However, KANs can be …
Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks
Physics-informed deep learning has emerged as a promising alternative for solving partial
differential equations. However, for complex problems, training these networks can still be …
differential equations. However, for complex problems, training these networks can still be …
[PDF][PDF] Multifidelity domain decomposition-based physics-informed neural networks for time-dependent problems
Multiscale problems are challenging for neural network-based discretizations of differential
equations, such as physics-informed neural networks (PINNs). This can be (partly) attributed …
equations, such as physics-informed neural networks (PINNs). This can be (partly) attributed …
What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications
Physics-informed deep operator networks (DeepONets) have emerged as a promising
approach toward numerically approximating the solution of partial differential equations …
approach toward numerically approximating the solution of partial differential equations …