A deep learning based approach to reduced order modeling for turbulent flow control using LSTM neural networks AT Mohan, DV Gaitonde arXiv preprint arXiv:1804.09269, 2018 | 299 | 2018 |
Compressed convolutional LSTM: An efficient deep learning framework to model high fidelity 3D turbulence A Mohan, D Daniel, M Chertkov, D Livescu arXiv preprint arXiv:1903.00033, 2019 | 131 | 2019 |
Embedding hard physical constraints in neural network coarse-graining of three-dimensional turbulence AT Mohan, N Lubbers, M Chertkov, D Livescu Physical Review Fluids 8 (1), 014604, 2023 | 129* | 2023 |
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 |
From deep to physics-informed learning of turbulence: Diagnostics R King, O Hennigh, A Mohan, M Chertkov arXiv preprint arXiv:1810.07785, 2018 | 59 | 2018 |
Model reduction and analysis of deep dynamic stall on a plunging airfoil AT Mohan, DV Gaitonde, MR Visbal Computers & Fluids 129 (28 April 2016), 1–19, 2016 | 57 | 2016 |
Spatio-temporal deep learning models of 3D turbulence with physics informed diagnostics AT Mohan, D Tretiak, M Chertkov, D Livescu Journal of Turbulence 21 (9-10), 484-524, 2020 | 52 | 2020 |
Analysis of airfoil stall control using dynamic mode decomposition AT Mohan, DV Gaitonde Journal of Aircraft 54 (4), 1508-1520, 2017 | 39 | 2017 |
Nuclear masses learned from a probabilistic neural network AE Lovell, AT Mohan, TM Sprouse, MR Mumpower Physical Review C 106 (1), 014305, 2022 | 34 | 2022 |
Physically interpretable machine learning for nuclear masses MR Mumpower, TM Sprouse, AE Lovell, AT Mohan Physical Review C 106 (2), L021301, 2022 | 31 | 2022 |
Quantifying uncertainties on fission fragment mass yields with mixture density networks AE Lovell, AT Mohan, P Talou Journal of Physics G: Nuclear and Particle Physics 47 (11), 114001, 2020 | 29 | 2020 |
Foresight: analysis that matters for data reduction P Grosset, CM Biwer, J Pulido, AT Mohan, A Biswas, J Patchett, TL Turton, ... SC20: International Conference for High Performance Computing, Networking …, 2020 | 28 | 2020 |
Embedding hard physical constraints in convolutional neural networks for 3D turbulence AT Mohan, N Lubbers, D Livescu, M Chertkov ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020 | 27 | 2020 |
Model reduction and analysis of deep dynamic stall on a plunging airfoil using dynamic mode decomposition AT Mohan, MR Visbal, DV Gaitonde 53rd AIAA Aerospace Sciences Meeting, 1058, 2015 | 22 | 2015 |
Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow V Shankar, GD Portwood, AT Mohan, PP Mitra, D Krishnamurthy, ... Physics of Fluids 34 (11), 2022 | 18* | 2022 |
Constraining fission yields using machine learning A Lovell, A Mohan, P Talou, M Chertkov EPJ Web of Conferences 211, 04006, 2019 | 10 | 2019 |
Development of the Senseiver for efficient field reconstruction from sparse observations JE Santos, ZR Fox, A Mohan, D O’Malley, H Viswanathan, N Lubbers Nature Machine Intelligence 5 (11), 1317-1325, 2023 | 9 | 2023 |
Learning stable Galerkin models of turbulence with differentiable programming AT Mohan, K Nagarajan, D Livescu arXiv preprint arXiv:2107.07559, 2021 | 6 | 2021 |
Bayesian averaging for ground state masses of atomic nuclei in a machine learning approach M Mumpower, M Li, TM Sprouse, BS Meyer, AE Lovell, AT Mohan Frontiers in Physics 11, 1198572, 2023 | 5 | 2023 |
Wavelet-powered neural networks for turbulence AT Mohan, D Livescu, M Chertkov ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020 | 5 | 2020 |