Arvind T. Mohan

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	Wszystkie	Od 2020
Cytowania	1330	1233
h-indeks	16	15
i10-indeks	18	17

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Daniel LivescuScientist, Los Alamos National LaboratoryZweryfikowany adres z lanl.gov
Michael (Misha) ChertkovChair, Program in Applied Mathematics, University of ArizonaZweryfikowany adres z math.arizona.edu
Datta GaitondeProfessor, The Ohio State UniversityZweryfikowany adres z osu.edu
Amy LovellLos Alamos National LaboratoryZweryfikowany adres z lanl.gov
Nicholas LubbersComputer, Computational, and Statistical Sciences Division, Los Alamos National LaboratoryZweryfikowany adres z lanl.gov
Trevor SprouseTheoretical Division, Los Alamos National LaboratoryZweryfikowany adres z lanl.gov
Matthew MumpowerStaff Scientist, Los Alamos National LaboratoryZweryfikowany adres z mumpower.net
Romit MaulikAssistant Professor and ICDS Co-Hire: Pennsylvania State UniversityZweryfikowany adres z psu.edu
Miguel VisbalAir Force Research LaboratoryZweryfikowany adres z us.af.mil
Sandeep MadireddyMathematics and Computer Science Division, Argonne National LaboratoryZweryfikowany adres z mcs.anl.gov
Bethany LuschArgonne National LabZweryfikowany adres z anl.gov
Prasanna BalaprakashDirector of AI Programs and Distinguished R&D Staff Scientist, Oak Ridge National LaboratoryZweryfikowany adres z ornl.gov
Patrick TalouLos Alamos National LaboratoryZweryfikowany adres z lanl.gov
Javier E. SantosCenter for Nonlinear Studies, Los Alamos National LabZweryfikowany adres z lanl.gov
Dima TretiakUniversity of WashingtonZweryfikowany adres z uw.edu
Gavin D PortwoodLawrence Livermore National LaboratoryZweryfikowany adres z llnl.gov
David SchmidtUniversity of MassachusettsZweryfikowany adres z acad.umass.edu
Varun ShankarCarnegie Mellon UniversityZweryfikowany adres z cmu.edu
Peetak MitraPrincipal AI Scientist, Equilibrium EnergyZweryfikowany adres z equilibriumenergy.com
Ryan N. KingNational Renewable Energy LaboratoryZweryfikowany adres z nrel.gov

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Arvind T. Mohan

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

Zweryfikowany adres z lanl.gov

Reduced Order Modeling Machine Learning Aerodynamics Computational Physics


Tytuł Sortuj wg cytatów Sortuj wg roku Sortuj wg tytułu	Cytowane przez Cytowane przez	Rok
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	333	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	140*	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	138	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	60	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	15	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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