Arvind T. Mohan
Arvind T. Mohan
Scientist, Computational Physics and Methods Group, Los Alamos National Laboratory
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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
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
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
Embedding hard physical constraints in neural network coarse-graining of 3D turbulence
AT Mohan, N Lubbers, D Livescu, M Chertkov
arXiv preprint arXiv:2002.00021, 2020
From deep to physics-informed learning of turbulence: Diagnostics
R King, O Hennigh, A Mohan, M Chertkov
arXiv preprint arXiv:1810.07785, 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
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
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
Analysis of airfoil stall control using dynamic mode decomposition
AT Mohan, DV Gaitonde
Journal of Aircraft 54 (4), 1508-1520, 2017
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
Constraining fission yields using machine learning
A Lovell, A Mohan, P Talou, M Chertkov
EPJ Web of Conferences 211, 04006, 2019
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
Learning stable galerkin models of turbulence with differentiable programming
AT Mohan, K Nagarajan, D Livescu
arXiv preprint arXiv:2107.07559, 2021
Learning non-linear spatio-temporal dynamics with convolutional Neural ODEs
V Shankar, G Portwood, A Mohan, P Mitra, C Rackauckas, L Wilson, ...
Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), 2020
A preliminary spectral decomposition and scale separation analysis of a high-fidelity dynamic stall dataset
AT Mohan, LM Agostini, MR Visbal, DV Gaitonde
54th AIAA Aerospace Sciences Meeting, 1352, 2016
Statistical Analysis and Model Reduction of Surface Pressure for Interaction of a Streamwise-Oriented Vortex with a Wing
AT Mohan, L Agostini, DV Gaitonde, DJ Garmann
22nd AIAA Computational Fluid Dynamics Conference, 3412, 2015
Wavelet-powered neural networks for turbulence
AT Mohan, D Livescu, M Chertkov
ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020
Learning Physics-based Galerkin models of turbulence with Neural Differential Equations
A Mohan, K Nagarajan, D Livescu
APS Division of Fluid Dynamics Meeting Abstracts, S01. 029, 2020
Rapid Spatiotemporal Turbulence Modeling with Convolutional Neural ODEs
V Shankar, G Portwood, A Mohan, P Mitra, V Viswanathan, D Schmidt
APS Division of Fluid Dynamics Meeting Abstracts, X11. 004, 2020
Model Reduction and Analysis of NS-DBD Based Control of Stalled NACA0015 Airfoil
AT Mohan, DV Gaitonde
Fluids Engineering Division Summer Meeting 46216, V01AT09A001, 2014
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Articles 1–20