4 papers
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…
Continual learning benefits from multiple sleep mechanisms: NREM, REM, and Synaptic Downscaling
Brian S. Robinson, Clare W. Lau, Alexander New +4
Learning new tasks and skills in succession without losing prior learning (i.e., catastrophic forgetting) is a computational challenge for both artificial and biological neural net…
Neural Basis Functions for Accelerating Solutions to High Mach Euler Equations
David Witman, Alexander New, Hicham Alkendry +1
We propose an approach to solving partial differential equations (PDEs) using a set of neural networks which we call Neural Basis Functions (NBF). This NBF framework is a novel var…
Curvature-informed multi-task learning for graph networks
Alexander New, Michael J. Pekala, Nam Q. Le +3
Properties of interest for crystals and molecules, such as band gap, elasticity, and solubility, are generally related to each other: they are governed by the same underlying laws…