| Deep learning for universal linear embeddings of nonlinear dynamics B Lusch, JN Kutz, SL Brunton Nature communications 9 (1), 4950, 2018 | 1221 | 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 | 771 | 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 | 236 | 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 | 124 | 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 | 100* | 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 | 61 | 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 | 50 | 2020 |
| 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 | 45 | 2022 |
| Inferring connectivity in networked dynamical systems: Challenges using Granger causality B Lusch, PD Maia, JN Kutz Physical Review E 94 (3), 032220, 2016 | 45 | 2016 |
| Non-autoregressive time-series methods for stable parametric reduced-order models R Maulik, B Lusch, P Balaprakash Physics of Fluids 32 (8), 2020 | 30 | 2020 |
| Deploying deep learning in OpenFOAM with TensorFlow R Maulik, H Sharma, S Patel, B Lusch, E Jennings AIAA Scitech 2021 Forum, 1485, 2021 | 25 | 2021 |
| 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 | 23* | 2022 |
| 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 |
| 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 | 19 | 2019 |
| 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 | 16 | 2020 |
| 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 | 12 | 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 | 12 | 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 | 11 | 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 | 6 | 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 | 5 | 2023 |
J. Nathan KutzProfessor of Applied Mathematics & Electrical and Computer Engineering在 uw.edu 的电子邮件经过验证
