2 papers
stat.ML2022
Tunable Complexity Benchmarks for Evaluating Physics-Informed Neural Networks on Coupled Ordinary Differential Equations
Alexander New, Benjamin Eng, Andrea C. Timm +1
In this work, we assess the ability of physics-informed neural networks (PINNs) to solve increasingly-complex coupled ordinary differential equations (ODEs). We focus on a pair of…
cs.LG2021
Geometry and Generalization: Eigenvalues as predictors of where a network will fail to generalize
Susama Agarwala, Benjamin Dees, Andrew Gearhart +1
We study the deformation of the input space by a trained autoencoder via the Jacobians of the trained weight matrices. In doing so, we prove bounds for the mean squared errors for…