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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
3242018
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
1392019
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
138*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
1362020
From deep to physics-informed learning of turbulence: Diagnostics
R King, O Hennigh, A Mohan, M Chertkov
arXiv preprint arXiv:1810.07785, 2018
612018
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
592016
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
562020
Nuclear masses learned from a probabilistic neural network
AE Lovell, AT Mohan, TM Sprouse, MR Mumpower
Physical Review C 106 (1), 014305, 2022
442022
Analysis of airfoil stall control using dynamic mode decomposition
AT Mohan, DV Gaitonde
Journal of Aircraft 54 (4), 1508-1520, 2017
402017
Physically interpretable machine learning for nuclear masses
MR Mumpower, TM Sprouse, AE Lovell, AT Mohan
Physical Review C 106 (2), L021301, 2022
382022
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
352020
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
312020
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
292020
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
22*2022
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
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
212023
Machine Learning technique for isotopic determination of radioisotopes using HPGe γ-ray spectra
A Khatiwada, M Klasky, M Lombardi, J Matheny, A Mohan
Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 2023
102023
Constraining fission yields using machine learning
A Lovell, A Mohan, P Talou, M Chertkov
EPJ Web of Conferences 211, 04006, 2019
102019
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
92023
Wavelet-powered neural networks for turbulence
AT Mohan, D Livescu, M Chertkov
ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020
72020
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