15 citations · 20 across the 6 of their papers we have counts for
3 papers · 1 filter
SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks
Bruno Jacob, Amanda A. Howard, Panos Stinis
Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs) in scientific computing. While PINNs typically use mul…
Multifidelity Kolmogorov-Arnold Networks
Amanda A. Howard, Bruno Jacob, Panos Stinis
We develop a method for multifidelity Kolmogorov-Arnold networks (KANs), which use a low-fidelity model along with a small amount of high-fidelity data to train a model for the hig…
Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems
Amanda A. Howard, Bruno Jacob, Sarah Helfert +2
Kolmogorov-Arnold networks (KANs) have attracted attention recently as an alternative to multilayer perceptrons (MLPs) for scientific machine learning. However, KANs can be expensi…