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Arvind T. Mohan
Arvind T. Mohan
Scientist, Computational Physics and Methods Group, Los Alamos National Laboratory
Verified email at lanl.gov
Title
Cited by
Cited by
Year
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
2992018
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
1312019
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
1242020
From deep to physics-informed learning of turbulence: Diagnostics
R King, O Hennigh, A Mohan, M Chertkov
arXiv preprint arXiv:1810.07785, 2018
592018
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
572016
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
522020
Analysis of airfoil stall control using dynamic mode decomposition
AT Mohan, DV Gaitonde
Journal of Aircraft 54 (4), 1508-1520, 2017
392017
Nuclear masses learned from a probabilistic neural network
AE Lovell, AT Mohan, TM Sprouse, MR Mumpower
Physical Review C 106 (1), 014305, 2022
342022
Physically interpretable machine learning for nuclear masses
MR Mumpower, TM Sprouse, AE Lovell, AT Mohan
Physical Review C 106 (2), L021301, 2022
312022
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
292020
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
282020
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
272020
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
222015
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
102019
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
92023
Learning stable Galerkin models of turbulence with differentiable programming
AT Mohan, K Nagarajan, D Livescu
arXiv preprint arXiv:2107.07559, 2021
62021
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
52023
Wavelet-powered neural networks for turbulence
AT Mohan, D Livescu, M Chertkov
ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020
52020
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