Följ
Arvind T. Mohan
Arvind T. Mohan
Scientist, Computational Physics and Methods Group, Los Alamos National Laboratory
Verifierad e-postadress på lanl.gov
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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
2822018
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
1242019
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
1222020
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
120*2023
From deep to physics-informed learning of turbulence: Diagnostics
R King, O Hennigh, A Mohan, M Chertkov
arXiv preprint arXiv:1810.07785, 2018
582018
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
552016
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
512020
Analysis of airfoil stall control using dynamic mode decomposition
AT Mohan, DV Gaitonde
Journal of Aircraft 54 (4), 1508-1520, 2017
372017
Nuclear masses learned from a probabilistic neural network
AE Lovell, AT Mohan, TM Sprouse, MR Mumpower
Physical Review C 106 (1), 014305, 2022
292022
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
262020
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
242020
Physically interpretable machine learning for nuclear masses
MR Mumpower, TM Sprouse, AE Lovell, AT Mohan
Physical Review C 106 (2), L021301, 2022
232022
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
16*2022
Constraining fission yields using machine learning
A Lovell, A Mohan, P Talou, M Chertkov
EPJ Web of Conferences 211, 04006, 2019
102019
Learning stable Galerkin models of turbulence with differentiable programming
AT Mohan, K Nagarajan, D Livescu
arXiv preprint arXiv:2107.07559, 2021
52021
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
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
32023
Data-Driven Mori-Zwanzig: Approaching a Reduced Order Model for Hypersonic Boundary Layer Transition
M Woodward, Y Tian, AT Mohan, YT Lin, C Hader, D Livescu, HF Fasel, ...
AIAA SCITECH 2023 Forum, 1624, 2023
32023
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Artiklar 1–20