29 citations · 29 across the 3 of their papers we have counts for
3 papers
physics.flu-dyn2023
A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty
Atul Agrawal, Phaedon-Stelios Koutsourelakis
We propose a data-driven, closure model for Reynolds-averaged Navier-Stokes (RANS) simulations that incorporates aleatoric, model uncertainty. The proposed closure consists of two…
cs.CE2021
A reduced-order modeling framework for simulating signatures of faults in a bladed disk
Divya Shyam Singh, Atul Agrawal, D. Roy Mahapatra
This paper reports a reduced-order modeling framework of bladed disks on a rotating shaft to simulate the vibration signature of faults like cracks in different components aiming t…
cs.LG2021★ 29 cited
Physics-aware deep neural networks for surrogate modeling of turbulent natural convection
Didier Lucor, Atul Agrawal, Anne Sergent
Recent works have explored the potential of machine learning as data-driven turbulence closures for RANS and LES techniques. Beyond these advances, the high expressivity and agilit…