Bethany Lusch

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J. Nathan KutzProfessor of Applied Mathematics & Electrical and Computer EngineeringVerified email at uw.edu
Steven L. BruntonProfessor, University of WashingtonVerified email at uw.edu
Prasanna BalaprakashDirector of AI Programs and Distinguished R&D Staff Scientist, Oak Ridge National LaboratoryVerified email at ornl.gov
Saumil PatelAssistant Computational Scientist, Argonne National LaboratoryVerified email at anl.gov
Venkatram VishwanathComputer Scientist, Argonne National LaboratoryVerified email at anl.gov
Elise JenningsIBM ResearchVerified email at ibm.com
Sandeep MadireddyMathematics and Computer Science Division, Argonne National LaboratoryVerified email at mcs.anl.gov
Gina M. MagnottiSenior Manager, AccentureVerified email at accenture.com
Roberto TorelliSenior Research Scientist, Ph.D - Argonne National LaboratoryVerified email at anl.gov
Vishwas RaoArgonne National LaboratoryVerified email at mcs.anl.gov
Arvind T. MohanScientist, Computational Physics and Methods Group, Los Alamos National LaboratoryVerified email at lanl.gov
Daniel LivescuScientist, Los Alamos National LaboratoryVerified email at lanl.gov
Murali Krishna EmaniComputer Scientist at Argonne National LaboratoryVerified email at anl.gov
Michael E. PapkaUniversity of Illinois Chicago / Argonne National Laboratory / University of ChicagoVerified email at uic.edu
Petro Junior MilanSambaNova.ai, Georgia TechVerified email at gatech.edu
Romain EgeleArgonne National Laboratory and Université Paris-SaclayVerified email at universite-paris-saclay.fr
Kwan-Liu MaProfessor of Computer Science, University of California at DavisVerified email at cs.ucdavis.edu
Shilpika (FNU Shilpika)Argonne National Laboratory & University of California, DavisVerified email at anl.gov
Pedro Doria MaiaAssistant Professor of Mathematics, UT ArlingtonVerified email at uta.edu
Craig GinNorth Carolina State UniversityVerified email at ncsu.edu

Bethany Lusch

Argonne National Lab

Verified email at anl.gov - Homepage

machine learning optimization scientific computing data science


Title Sort by citations Sort by year Sort by 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), 4950, 2018	1444	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	931	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), 2021	298	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	138	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	111*	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	70	2021
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	57	2022
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	57	2020
Inferring connectivity in networked dynamical systems: Challenges using Granger causality B Lusch, PD Maia, JN Kutz Physical Review E 94 (3), 032220, 2016	48	2016
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	32	2020
Deploying deep learning in OpenFOAM with TensorFlow R Maulik, H Sharma, S Patel, B Lusch, E Jennings AIAA Scitech 2021 Forum, 1485, 2021	29	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	21	2020
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	21	2019
Submodular Hamming Metrics JA Gillenwater, RK Iyer, B Lusch, R Kidambi, JA Bilmes Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015	20	2015
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	15	2022
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	15	2021
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	12	2018
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	11	2023
Computationally efficient data-driven discovery and linear representation of nonlinear systems for control M Tiwari, G Nehma, B Lusch IEEE Control Systems Letters, 2023	8	2023

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