Bethany Lusch
Tytuł
Cytowane przez
Cytowane przez
Rok
Deep learning for universal linear embeddings of nonlinear dynamics
B Lusch, JN Kutz, SL Brunton
Nature communications 9 (1), 1-10, 2018
2392018
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
672019
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
312020
Inferring connectivity in networked dynamical systems: Challenges using Granger causality
B Lusch, PD Maia, JN Kutz
Physical Review E 94 (3), 032220, 2016
202016
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
172020
Submodular hamming metrics
JA Gillenwater, RK Iyer, B Lusch, R Kidambi, JA Bilmes
Advances in Neural Information Processing Systems, 3141-3149, 2015
162015
Deep learning models for global coordinate transformations that linearize PDEs
C Gin, B Lusch, SL Brunton, JN Kutz
arXiv preprint arXiv:1911.02710, 2019
102019
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
92019
Recurrent Neural Network Architecture Search for Geophysical Emulation
R Maulik, R Egele, B Lusch, P Balaprakash
arXiv preprint arXiv:2004.10928, 2020
62020
Deep learning for universal linear embeddings of nonlinear dynamics Nat
B Lusch, J Nathan Kutz, SL Brunton
Commun 9, 1-10, 2018
52018
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
Non-autoregressive time-series methods for stable parametric reduced-order models
R Maulik, B Lusch, P Balaprakash
Physics of Fluids 32 (8), 087115, 2020
32020
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
22019
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
Shape constrained tensor decompositions using sparse representations in over-complete libraries
B Lusch, EC Chi, JN Kutz
arXiv preprint arXiv:1608.04674, 2016
12016
A turbulent eddy-viscosity surrogate modeling framework for Reynolds-Averaged Navier-Stokes simulations
R Maulik, H Sharma, S Patel, B Lusch, E Jennings
Computers & Fluids, 104777, 2020
2020
DATA-DRIVEN MODEL REDUCTION OF MULTIPHASE FLOW IN A SINGLE-HOLE AUTOMOTIVE INJECTOR
PJ Milan, R Torelli, B Lusch, GM Magnotti
Atomization and Sprays 30 (6), 2020
2020
Machine learning of sequential data for non-intrusive reduced-order models
R Maulik, A Mohan, S Madireddy, B Lusch, P Balaprakash, D Livescu
APS, H10. 003, 2019
2019
Autoencoders for discovering coordinates and dynamics from data
K Champion, B Lusch, N Kutz, S Brunton
APS, P10. 006, 2019
2019
Koopman operator approximations for PDEs using deep learning
C Gin, B Lusch, S Brunton, N Kutz
APS, L10. 005, 2019
2019
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