From Deep to Physics-Informed Learning of Turbulence: Diagnostics. - Ryan King; Oliver Hennigh; Arvind T. Mohan; Michael Chertkov (CoRR, 2018)

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; et al. (CoRR, 2019)

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 001; Daniel Livescu; James P. Ahrens (SC, 2020)

Physics-Constrained Generative Adversarial Networks for 3D Turbulence. - Dima Tretiak; Arvind T. Mohan; Daniel Livescu (CoRR, 2022)

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., 2023)

Machine learned reconstruction of tsunami dynamics from sparse observations. - Edward McDugald; Arvind T. Mohan; Darren Engwirda; Agnese Marcato; Javier E. Santos (CoRR, 2024)

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)

Binned Spectral Power Loss for Improved Prediction of Chaotic Systems. - Dibyajyoti Chakraborty; Arvind T. Mohan; Romit Maulik (CoRR, 2025)