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Aniruddhe Pradhan
Aniruddhe Pradhan
University of Michigan
Verified email at iitkgp.ac.in
Title
Cited by
Cited by
Year
Conditionally parameterized, discretization-aware neural networks for mesh-based modeling of physical systems
J Xu, A Pradhan, K Duraisamy
Advances in Neural Information Processing Systems 34, 1634-1645, 2021
282021
Large Eddy Simulation Using Lattice Boltzmann Method Based on Sigma Model
A Pradhan, S Yadav
Procedia Engineering 127, 177–184, 2015
122015
Variational multiscale super‐resolution: A data‐driven approach for reconstruction and predictive modeling of unresolved physics
A Pradhan, K Duraisamy
International Journal for Numerical Methods in Engineering 124 (19), 4339-4370, 2023
82023
Variational multiscale closures for finite element discretizations using the Mori–Zwanzig approach
A Pradhan, K Duraisamy
Computer Methods in Applied Mechanics and Engineering 368, 113152, 2020
42020
A unified understanding of scale-resolving simulations and near-wall modelling of turbulent flows using optimal finite-element projections
A Pradhan, K Duraisamy
Journal of Fluid Mechanics 955, A6, 2023
32023
Arbitrary Order Solutions for the Eikonal Equation using a Discontinuous Galerkin Method
D Flad, A Pradhan, S Murman
arXiv preprint arXiv:2108.05950, 2021
22021
Mathematical Formalisms and Data-Driven Approaches for Coarse-Graining of Multi-Scale Finite Element Discretizations
A Pradhan
12022
A Neural-Network Based Adaptive Discontinuous Galerkin Method for Turbulent Flow Simulations
MJ McGruder, A Pradhan, K Fidkowski
AIAA SCITECH 2023 Forum, 1802, 2023
2023
Improved slip wall models using optimal finite element projections.
A Pradhan, K Duraisamy
Bulletin of the American Physical Society, 2022
2022
Optimal wall-modeling strategies using data.
A Pradhan, K Duraisamy
APS Division of Fluid Dynamics Meeting Abstracts, M28. 003, 2021
2021
Super-resolution of Finite Element spaces using Physics-informed Deep Learning Networks for Turbulent flows
A Pradhan, R Biswas, K Duraisamy
APS Division of Fluid Dynamics Meeting Abstracts, S01. 001, 2020
2020
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