Bethany Lusch
Title
Cited by
Cited by
Year
Deep learning for universal linear embeddings of nonlinear dynamics
B Lusch, JN Kutz, SL Brunton
Nature communications 9 (1), 1-10, 2018
1302018
Inferring connectivity in networked dynamical systems: Challenges using Granger causality
B Lusch, PD Maia, JN Kutz
Physical Review E 94 (3), 032220, 2016
172016
Data-driven discovery of coordinates and governing equations
K Champion, B Lusch, JN Kutz, SL Brunton
Proceedings of the National Academy of Sciences 116 (45), 22445-22451, 2019
142019
Submodular hamming metrics
JA Gillenwater, RK Iyer, B Lusch, R Kidambi, JA Bilmes
Advances in Neural Information Processing Systems, 3141-3149, 2015
142015
Time-series learning of latent-space dynamics for reduced-order model closure
R Maulik, A Mohan, B Lusch, S Madireddy, P Balaprakash, D Livescu
Physica D: Nonlinear Phenomena 405, 132368, 2020
122020
Modeling cognitive deficits following neurodegenerative diseases and traumatic brain injuries with deep convolutional neural networks
B Lusch, J Weholt, PD Maia, JN Kutz
Brain and cognition 123, 154-164, 2018
42018
Deep Learning Models for Global Coordinate Transformations that Linearize PDEs
C Gin, B Lusch, SL Brunton, JN Kutz
arXiv preprint arXiv:1911.02710, 2019
32019
Accelerating RANS turbulence modeling using potential flow and machine learning
R Maulik, H Sharma, S Patel, B Lusch, E Jennings
arXiv preprint arXiv:1910.10878, 2019
32019
Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders
R Maulik, B Lusch, P Balaprakash
arXiv preprint arXiv:2002.00470, 2020
12020
MELA: A Visual Analytics Tool for Studying Multifidelity HPC System Logs
FNU Shilpika, B Lusch, M Emani, V Vishwanath, ME Papka, KL Ma
2019 IEEE/ACM Industry/University Joint International Workshop on Data …, 2019
12019
Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models
R Maulik, V Rao, S Madireddy, B Lusch, P Balaprakash
arXiv preprint arXiv:1909.09144, 2019
12019
Shape constrained tensor decompositions using sparse representations in over-complete libraries
B Lusch, EC Chi, JN Kutz
arXiv preprint arXiv:1608.04674, 2016
12016
Machine learning of sequential data for non-intrusive reduced-order models
R Maulik, A Mohan, S Madireddy, B Lusch, P Balaprakash, D Livescu
Bulletin of the American Physical Society, 2019
2019
Autoencoders for discovering coordinates and dynamics from data
K Champion, B Lusch, N Kutz, S Brunton
Bulletin of the American Physical Society, 2019
2019
Koopman operator approximations for PDEs using deep learning
C Gin, B Lusch, S Brunton, N Kutz
Bulletin of the American Physical Society, 2019
2019
Shape Constrained Tensor Decompositions
B Lusch, EC Chi, JN Kutz
2019 IEEE International Conference on Data Science and Advanced Analytics …, 2019
2019
Data-driven discovery of Koopman eigenfunctions using deep learning
B Lusch, SL Brunton, JN Kutz
APS Division of Fluid Dynamics Meeting Abstracts, 2017
2017
Machine learning and data decompositions for complex networked dynamical systems
BA Lusch
2016
MELA: A Visual Analytics Tool for Studying Multifidelity HPC System Logs
B Lusch, M Emani, V Vishwanath, ME Papka, KL Ma
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Articles 1–19