Bethany Lusch
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Deep learning for universal linear embeddings of nonlinear dynamics
B Lusch, JN Kutz, SL Brunton
Nature communications 9 (1), 1-10, 2018
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
Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders
R Maulik, B Lusch, P Balaprakash
Physics of Fluids 33 (3), 037106, 2021
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
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 227, 104777, 2021
Inferring connectivity in networked dynamical systems: Challenges using Granger causality
B Lusch, PD Maia, JN Kutz
Physical Review E 94 (3), 032220, 2016
Deep learning models for global coordinate transformations that linearise PDEs
C Gin, B Lusch, SL Brunton, JN Kutz
European Journal of Applied Mathematics 32 (3), 515-539, 2021
Recurrent neural network architecture search for geophysical emulation
R Maulik, R Egele, B Lusch, P Balaprakash
SC20: International Conference for High Performance Computing, Networking†…, 2020
Non-autoregressive time-series methods for stable parametric reduced-order models
R Maulik, B Lusch, P Balaprakash
Physics of Fluids 32 (8), 087115, 2020
Submodular Hamming Metrics
JA Gillenwater, RK Iyer, B Lusch, R Kidambi, JA Bilmes
Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015
Deploying deep learning in OpenFOAM with TensorFlow
R Maulik, H Sharma, S Patel, B Lusch, E Jennings
AIAA Scitech 2021 Forum, 1485, 2021
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
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
Accelerating the generation of static coupling injection maps using a data-driven emulator
S Mondal, R Torelli, B Lusch, PJ Milan, GM Magnotti
SAE International Journal of Advances and Current Practices in Mobility 3†…, 2021
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
AIEADA 1.0: Efficient high-dimensional variational data assimilation with machine-learned reduced-order models
R Maulik, V Rao, J Wang, G Mengaldo, E Constantinescu, B Lusch, ...
Geoscientific Model Development Discussions 2022, 1-20, 2022
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification
R Egele, R Maulik, K Raghavan, P Balaprakash, B Lusch
arXiv preprint arXiv:2110.13511, 2021
PythonFOAM: In-situ data analyses with OpenFOAM and Python
R Maulik, DK Fytanidis, B Lusch, V Vishwanath, S Patel
Journal of Computational Science 62, 101750, 2022
Machine Learning-Enabled Prediction of Transient Injection Map In Automotive Injectors With Uncertainty Quantification
S Mondal, GM Magnotti, B Lusch, R Maulik, R Torelli
Internal Combustion Engine Division Fall Technical Conference 85512, V001T03A005, 2021
Data-Driven Modeling of Large-Eddy Simulations for Fuel Injector Design
PJ Milan, S Mondal, R Torelli, B Lusch, R Maulik, GM Magnotti
AIAA Scitech 2021 Forum, 1016, 2021
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