106 citations · 166 across the 14 of their papers we have counts for
14 papers
Reversible and irreversible bracket-based dynamics for deep graph neural networks
Anthony Gruber, Kookjin Lee, Nathaniel Trask
Recent works have shown that physics-inspired architectures allow the training of deep graph neural networks (GNNs) without oversmoothing. The role of these physics is unclear, how…
Probabilistic partition of unity networks for high-dimensional regression problems
Tiffany Fan, Nathaniel Trask, Marta D'Elia +1
We explore the probabilistic partition of unity network (PPOU-Net) model in the context of high-dimensional regression problems and propose a general framework focusing on adaptive…
Parameter-varying neural ordinary differential equations with partition-of-unity networks
Kookjin Lee, Nathaniel Trask
In this study, we propose parameter-varying neural ordinary differential equations (NODEs) where the evolution of model parameters is represented by partition-of-unity networks (PO…
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter
Ruben Villarreal, Nikolaos N. Vlassis, Nhon N. Phan +5
Experimental data is costly to obtain, which makes it difficult to calibrate complex models. For many models an experimental design that produces the best calibration given a limit…
Scalable algorithms for physics-informed neural and graph networks
Khemraj Shukla, Mengjia Xu, Nathaniel Trask +1
Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale proc…
Unsupervised physics-informed disentanglement of multimodal data for high-throughput scientific discovery
Nathaniel Trask, Carianne Martinez, Kookjin Lee +1
We introduce physics-informed multimodal autoencoders (PIMA) - a variational inference framework for discovering shared information in multimodal scientific datasets representative…