69 citations · 162 across the 24 of their papers we have counts for
5 papers · 1 filter
Model synthesis and identifiability analysis of stiff chemical reaction systems with inVAErt networks
Sreejata Dey, Guoxiang Grayson Tong, Jonathan F. MacArt +1
We consider the problem of learning data-driven replicas for stiff systems of ordinary differential equations arising in chemical kinetics that can be evaluated with high computati…
A TVD neural network closure and application to turbulent combustion
Seung Won Suh, Jonathan F MacArt, Luke N Olson +1
Trained neural networks (NN) have attractive features for closing governing equations. There are many methods that are showing promise, but all can fail in cases when small errors…
Sampling-based Distributed Training with Message Passing Neural Network
Priyesh Kakka, Sheel Nidhan, Rishikesh Ranade +2
In this study, we introduce a domain-decomposition-based distributed training and inference approach for message-passing neural networks (MPNN). Our objective is to address the cha…
PDE-constrained Models with Neural Network Terms: Optimization and Global Convergence
Justin Sirignano, Jonathan MacArt, Konstantinos Spiliopoulos
Recent research has used deep learning to develop partial differential equation (PDE) models in science and engineering. The functional form of the PDE is determined by a neural ne…
DPM: A deep learning PDE augmentation method (with application to large-eddy simulation)
Jonathan B. Freund, Jonathan F. MacArt, Justin Sirignano
Machine learning for scientific applications faces the challenge of limited data. We propose a framework that leverages a priori known physics to reduce overfitting when training o…