Arvind T. Mohan

According to our database, Arvind T. Mohan authored at least 8 papers between 2018 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Binned Spectral Power Loss for Improved Prediction of Chaotic Systems.

Dibyajyoti Chakraborty, Arvind T. Mohan, Romit Maulik

CoRR, February, 2025

Physics-constrained coupled neural differential equations for one dimensional blood flow modeling.

Hunor Csala, Arvind T. Mohan, Daniel Livescu, Amirhossein Arzani

Comput. Biol. Medicine, 2025

2024

Machine learned reconstruction of tsunami dynamics from sparse observations.

Edward McDugald, Arvind T. Mohan, Darren Engwirda, Agnese Marcato, Javier E. Santos

CoRR, 2024

2023

Development of the Senseiver for efficient field reconstruction from sparse observations.

Javier E. Santos, Zachary R. Fox, Arvind T. Mohan, Daniel O'Malley, Hari S. Viswanathan, Nicholas Lubbers

Nat. Mac. Intell., October, 2023

2022

Physics-Constrained Generative Adversarial Networks for 3D Turbulence.

Dima Tretiak, Arvind T. Mohan, Daniel Livescu

CoRR, 2022

2020

Foresight: analysis that matters for data reduction.

Pascal Grosset, Christopher M. Biwer, Jesus Pulido, Arvind T. Mohan, Ayan Biswas, John Patchett, Terece L. Turton, David H. Rogers, Daniel Livescu, James P. Ahrens

Proceedings of the International Conference for High Performance Computing, 2020

2019

The ISTI Rapid Response on Exploring Cloud Computing 2018.

Carleton Coffrin, James Arnold, Stephan J. Eidenbenz, Derek Aberle, John Ambrosiano, Zachary K. Baker, Sara Brambilla, Michael J. Brown, K. Nolan Carter, Pinghan Chu, Patrick Conry, Keeley Costigan, Ariane Eberhardt, David M. Fobes, Adam Gausmann, Sean N. Harris, Donovan Heimer, Marlin Holmes, Bill Junor, Csaba Kiss, Steve Linger, Rodman R. Linn, Li-Ta Lo, Jonathan MacCarthy, Omar Marcillo, Clay McGinnis, Alexander McQuarters, Eric Michalak, Arvind T. Mohan, Matt Nelson, Diane Oyen, Nidhi Parikh, Donatella Pasqualini, Aaron S. Pope, Reid B. Porter, Chris Rawlings, Hannah Reinbolt, Reid D. Rivenburgh, Philip Romero, Kevin Schoonover, Alexei N. Skurikhin, Daniel R. Tauritz, Dima Tretiak, Zhehui Wang, James Wernicke, Brad Wolfe, Phillip Wolfram, Jonathan Woodring

CoRR, 2019

2018

From Deep to Physics-Informed Learning of Turbulence: Diagnostics.

Ryan King, Oliver Hennigh, Arvind T. Mohan, Michael Chertkov

CoRR, 2018