research
∙
09/08/2023
Computationally Efficient Data-Driven Discovery and Linear Representation of Nonlinear Systems For Control
This work focuses on developing a data-driven framework using Koopman op...
share
research
∙
06/15/2023
A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of Multifidelity HPC Systems
The ability to monitor and interpret of hardware system events and behav...
research
∙
10/26/2021
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification
Deep neural networks are powerful predictors for a variety of tasks. How...
research
∙
03/17/2021
PythonFOAM: In-situ data analyses with OpenFOAM and Python
In this article, we outline the development of a general-purpose Python-...
research
∙
11/07/2019
Deep Learning Models for Global Coordinate Transformations that Linearize PDEs
We develop a deep autoencoder architecture that can be used to find a co...
research
∙
09/18/2019
Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models
Rapid simulations of advection-dominated problems are vital for multiple...
research
∙
12/27/2017
Deep learning for universal linear embeddings of nonlinear dynamics
Identifying coordinate transformations that make strongly nonlinear dyna...
research
∙
08/16/2016
Shape Constrained Tensor Decompositions using Sparse Representations in Over-Complete Libraries
We consider N-way data arrays and low-rank tensor factorizations where t...
research
∙
11/06/2015
