Jan 28, 2022 · Here we train learned simulators at low spatial and temporal resolutions to capture turbulent dynamics generated at high resolution.
Dec 31, 2021 · Here we train learned simulators at low spatial and temporal resolutions to capture turbulent dynamics generated at high resolution.
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May 27, 2024 · Here we train learned simulators at low spatial and temporal resolutions to capture turbulent dynamics generated at high resolution. We show ...
Learned Coarse Models for Efficient Turbulence Simulation · 2D Incompressible Decaying Turbulence (Incomp-2D) · 3D Compressible Decaying Turbulence (CompDecay- ...
This paper trains turbulence models based on convolutional neural networks that achieve significant improvements of long-term a posteriori statistics when ...
Feb 13, 2023 · We develop a generative-adversarial-network (GAN)-based model to reconstruct the three-dimensional velocity fields from flow data.
These learned turbulence models improve under-resolved low resolution solutions to the incompressible Navier-Stokes equations at simulation time. Our study ...
Learned turbulence modelling with differentiable fluid solvers
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Sep 29, 2022 · In this paper, we train turbulence models based on convolutional neural networks. These learned turbulence models improve under-resolved low-resolution ...
Feb 14, 2022 · In this paper, we train turbulence models based on convolutional neural networks. These learned turbulence models improve under-resolved low resolution ...
We introduce the idea of adversarially robust machine learning to simulators for physical problems, and in particular the simulation of turbulence trajectories.