Obserwuj
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), 4950, 2018
14432018
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
9322019
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), 2021
2982021
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
1382020
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
110*2021
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
702021
Autodeuq: Automated deep ensemble with uncertainty quantification
R Egele, R Maulik, K Raghavan, B Lusch, I Guyon, P Balaprakash
2022 26th International Conference on Pattern Recognition (ICPR), 1908-1914, 2022
572022
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
572020
Inferring connectivity in networked dynamical systems: Challenges using Granger causality
B Lusch, PD Maia, JN Kutz
Physical Review E 94 (3), 032220, 2016
482016
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
33*2022
Non-autoregressive time-series methods for stable parametric reduced-order models
R Maulik, B Lusch, P Balaprakash
Physics of Fluids 32 (8), 2020
322020
Deploying deep learning in OpenFOAM with TensorFlow
R Maulik, H Sharma, S Patel, B Lusch, E Jennings
AIAA Scitech 2021 Forum, 1485, 2021
292021
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
212020
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
212019
Submodular Hamming Metrics
JA Gillenwater, RK Iyer, B Lusch, R Kidambi, JA Bilmes
Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015
202015
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
152022
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
152021
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
122018
Artificial intelligence guided studies of van der Waals magnets
TD Rhone, R Bhattarai, H Gavras, B Lusch, M Salim, M Mattheakis, ...
Advanced Theory and Simulations 6 (6), 2300019, 2023
112023
Computationally efficient data-driven discovery and linear representation of nonlinear systems for control
M Tiwari, G Nehma, B Lusch
IEEE Control Systems Letters, 2023
82023
Nie można teraz wykonać tej operacji. Spróbuj ponownie później.
Prace 1–20