1 citations · 2 across the 3 of their papers we have counts for
3 papers
physics.flu-dyn2024
Online Optimisation of Machine Learning Collision Models to Accelerate Direct Molecular Simulation of Rarefied Gas Flows
Nicholas Daultry Ball, Jonathan F. MacArt, Justin Sirignano
We develop an online optimisation algorithm for in situ calibration of collision models in simulations of rarefied gas flows. The online optimised collision models are able to achi…
physics.flu-dyn2023★ 1 cited
Adjoint-based machine learning for active flow control
Xuemin Liu, Jonathan F. MacArt
We develop neural-network active flow controllers using a deep learning PDE augmentation method (DPM). The sensitivities for optimization are computed using adjoints of the governi…
physics.flu-dyn2023★ 1 cited
Dynamic Deep Learning LES Closures: Online Optimization With Embedded DNS
Justin Sirignano, Jonathan F. MacArt
Deep learning (DL) has recently emerged as a candidate for closure modeling of large-eddy simulation (LES) of turbulent flows. High-fidelity training data is typically limited: it…