no code implementations • 23 Feb 2024 • Priyesh Kakka, Sheel Nidhan, Rishikesh Ranade, Jonathan F. MacArt
In this study, we introduce a domain-decomposition-based distributed training and inference approach for message-passing neural networks (MPNN).
no code implementations • 4 Mar 2023 • Justin Sirignano, Jonathan F. MacArt
The deep learning closure model is dynamically trained during a large-eddy simulation (LES) calculation using embedded direct numerical simulation (DNS) data.
no code implementations • 6 Aug 2022 • Justin Sirignano, Jonathan F. MacArt
A deep learning (DL) closure model for large-eddy simulation (LES) is developed and evaluated for incompressible flows around a rectangular cylinder at moderate Reynolds numbers.
no code implementations • 3 May 2021 • Jonathan F. MacArt, Justin Sirignano, Jonathan B. Freund
The weights of a deep neural network model are optimized in conjunction with the governing flow equations to provide a model for sub-grid-scale stresses in a temporally developing plane turbulent jet at Reynolds number $Re_0=6\, 000$.
no code implementations • 20 Nov 2019 • Jonathan B. Freund, Jonathan F. MacArt, Justin Sirignano
A deep neural network is embedded in a partial differential equation (PDE) that expresses the known physics and learns to describe the corresponding unknown or unrepresented physics from the data.