Work

Spiking Physics Informed Neural Networks on Loihi-2

Year: 2024

Type: report

Abstract: Machine learning methods inspire new numerical algorithms in scientific computing -Allow for data-driven discovery of scientific principles and models Deep Operator Networks

DOI: 10.2172/2586167

Authors: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States), Bradley Theilman, Qian Zhang, Adar Kahana, Eric Cyr, Nathaniel Trask, James Aimone, George Karniadakis

Institutions: Sandia National Laboratories, Brown University, University of Pennsylvania

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Funders: U.S. Department of Energy, National Nuclear Security Administration, Advanced Scientific Computing Research, Sandia National Laboratories

Awards: G11630931, G1166951225, G2214935549, G413247060, G4903105778, G5339743583, G855168022

OpenAlex ID: W4413766903

Cites:

Cited by: 2

Related to: 10