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
Datta GaitondeProfessor, The Ohio State UniversityVerified email at osu.edu
Amy LovellLos Alamos National LaboratoryVerified email at lanl.gov
Nicholas LubbersComputer, Computational, and Statistical Sciences Division, Los Alamos National LaboratoryVerified email at lanl.gov
Trevor SprouseTheoretical Division, Los Alamos National LaboratoryVerified email at lanl.gov
Matthew MumpowerStaff Scientist, Los Alamos National LaboratoryVerified email at mumpower.net
Romit MaulikAssistant Professor and ICDS Co-Hire: Pennsylvania State UniversityVerified email at psu.edu
Miguel VisbalAir Force Research LaboratoryVerified email at us.af.mil
Patrick TalouLos Alamos National LaboratoryVerified email at lanl.gov
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
Javier E. SantosCenter for Nonlinear Studies, Los Alamos National LabVerified email at lanl.gov
Dima TretiakUniversity of WashingtonVerified email at uw.edu
Gavin D PortwoodLawrence Livermore National LaboratoryVerified email at llnl.gov
David SchmidtUniversity of MassachusettsVerified email at acad.umass.edu
Varun ShankarCarnegie Mellon UniversityVerified email at cmu.edu
Peetak MitraPrincipal AI Scientist, Equilibrium EnergyVerified email at equilibriumenergy.com
Ryan N. KingNational Renewable Energy LaboratoryVerified email at nrel.gov

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


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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	332	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	145	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	139*	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	137	2020
From deep to physics-informed learning of turbulence: Diagnostics R King, O Hennigh, A Mohan, M Chertkov arXiv preprint arXiv:1810.07785, 2018	62	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	61	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	58	2020
Nuclear masses learned from a probabilistic neural network AE Lovell, AT Mohan, TM Sprouse, MR Mumpower Physical Review C 106 (1), 014305, 2022	46	2022
Analysis of airfoil stall control using dynamic mode decomposition AT Mohan, DV Gaitonde Journal of Aircraft 54 (4), 1508-1520, 2017	43	2017
Physically interpretable machine learning for nuclear masses MR Mumpower, TM Sprouse, AE Lovell, AT Mohan Physical Review C 106 (2), L021301, 2022	41	2022
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	38	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	31	2020
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	30	2020
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	25	2023
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	22	2015
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	14	2023
Constraining fission yields using machine learning A Lovell, A Mohan, P Talou, M Chertkov EPJ Web of Conferences 211, 04006, 2019	10	2019
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	9	2023
Wavelet-powered neural networks for turbulence AT Mohan, D Livescu, M Chertkov ICLR 2020 Workshop on Integration of Deep Neural Models and Differential …, 2020	8	2020

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