Arvind T. Mohan

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Daniel LivescuScientist, Los Alamos National LaboratoryVerified email at lanl.gov
Michael (Misha) ChertkovChair, Program in Applied Mathematics, University of ArizonaVerified email at math.arizona.edu
Amy LovellLos Alamos National LaboratoryVerified email at lanl.gov
Miguel VisbalAir Force Research LaboratoryVerified email at us.af.mil
Trevor SprouseTheoretical Division, Los Alamos National LaboratoryVerified email at lanl.gov
Matthew MumpowerStaff Scientist, Los Alamos National LaboratoryVerified email at mumpower.net
Sandeep MadireddyMathematics and Computer Science Division, Argonne National LaboratoryVerified email at mcs.anl.gov
Bethany LuschArgonne National LabVerified email at anl.gov
Prasanna BalaprakashDirector of AI Programs and Distinguished R&D Staff Scientist, Oak Ridge National LaboratoryVerified email at ornl.gov
Romit MaulikAssistant Professor - Pennsylvania State UniversityVerified email at psu.edu
Patrick TalouLos Alamos National LaboratoryVerified email at lanl.gov
Nicholas LubbersStaff Scientist, Computer, Computational, and Statistical Sciences Division, LANLVerified email at bu.edu
Dima TretiakGeorgia Institute of TechnologyVerified email at lanl.gov
Ryan N. KingNational Renewable Energy LaboratoryVerified email at nrel.gov
Varun ShankarCarnegie Mellon UniversityVerified email at cmu.edu
Lionel AgostiniCNRS - Pprime instituteVerified email at univ-poitiers.fr
Gavin D PortwoodLawrence Livermore National LaboratoryVerified email at llnl.gov
David SchmidtUniversity of MassachusettsVerified email at acad.umass.edu
Peetak P. MitraExcartaVerified email at excarta.io
Venkatasubramanian ViswanathanAssociate Professor, University of MichiganVerified email at umich.edu

Arvind T. Mohan

Scientist, Computational Physics and Methods Group, Los Alamos National Laboratory

Verified email at lanl.gov

Reduced Order Modeling Machine Learning Aerodynamics Computational Physics


Title Sort by citations Sort by year Sort by 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	267	2018
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	115	2020
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	115	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	104*	2023
From deep to physics-informed learning of turbulence: Diagnostics R King, O Hennigh, A Mohan, M Chertkov arXiv preprint arXiv:1810.07785, 2018	56	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	52	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	45	2020
Analysis of airfoil stall control using dynamic mode decomposition AT Mohan, DV Gaitonde Journal of Aircraft 54 (4), 1508-1520, 2017	36	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	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	24	2020
Nuclear masses learned from a probabilistic neural network AE Lovell, AT Mohan, TM Sprouse, MR Mumpower Physical Review C 106 (1), 014305, 2022	23	2022
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	23	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	23	2015
Physically interpretable machine learning for nuclear masses MR Mumpower, TM Sprouse, AE Lovell, AT Mohan Physical Review C 106 (2), L021301, 2022	19	2022
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	14*	2022
Constraining fission yields using machine learning A Lovell, A Mohan, P Talou, M Chertkov EPJ Web of Conferences 211, 04006, 2019	11	2019
Learning stable Galerkin models of turbulence with differentiable programming AT Mohan, K Nagarajan, D Livescu arXiv preprint arXiv:2107.07559, 2021	5	2021
Wavelet-powered neural networks for turbulence AT Mohan, D Livescu, M Chertkov ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020	4	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	4	2016
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	3	2023

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