Current and emerging deep-learning methods for the simulation of fluid dynamics
Over the last decade, deep learning (DL), a branch of machine learning, has experienced
rapid progress. Powerful tools for tasks that have been traditionally complex to automate …
rapid progress. Powerful tools for tasks that have been traditionally complex to automate …
Benchmarking of machine learning ocean subgrid parameterizations in an idealized model
Recently, a growing number of studies have used machine learning (ML) models to
parameterize computationally intensive subgrid‐scale processes in ocean models. Such …
parameterize computationally intensive subgrid‐scale processes in ocean models. Such …
Multiple physics pretraining for physical surrogate models
We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic
pretraining approach for physical surrogate modeling. MPP involves training large surrogate …
pretraining approach for physical surrogate modeling. MPP involves training large surrogate …
Rapid Permeability Upscaling of Digital Porous Media via Physics‐Informed Neural Networks
M Elmorsy, W El‐Dakhakhni… - Water Resources …, 2023 - Wiley Online Library
Subsurface processes are important in solving many of the grand challenges facing our
society today, including the sustainable extraction of hydrocarbons, the permanent …
society today, including the sustainable extraction of hydrocarbons, the permanent …
On fast simulation of dynamical system with neural vector enhanced numerical solver
The large-scale simulation of dynamical systems is critical in numerous scientific and
engineering disciplines. However, traditional numerical solvers are limited by the choice of …
engineering disciplines. However, traditional numerical solvers are limited by the choice of …
Neural ideal large eddy simulation: Modeling turbulence with neural stochastic differential equations
We introduce a data-driven learning framework that assimilates two powerful ideas: ideal
large eddy simulation (LES) from turbulence closure modeling and neural stochastic …
large eddy simulation (LES) from turbulence closure modeling and neural stochastic …
Turbulence Closure With Small, Local Neural Networks: Forced Two‐Dimensional and β‐Plane Flows
K Srinivasan, MD Chekroun… - Journal of Advances in …, 2024 - Wiley Online Library
We parameterize sub‐grid scale (SGS) fluxes in sinusoidally forced two‐dimensional
turbulence on the β‐plane at high Reynolds numbers (Re∼ 25,000) using simple 2‐layer …
turbulence on the β‐plane at high Reynolds numbers (Re∼ 25,000) using simple 2‐layer …
A neural pde solver with temporal stencil modeling
Numerical simulation of non-linear partial differential equations plays a crucial role in
modeling physical science and engineering phenomena, such as weather, climate, and …
modeling physical science and engineering phenomena, such as weather, climate, and …
Neural spectral methods: Self-supervised learning in the spectral domain
Y Du, N Chalapathi, A Krishnapriyan - arXiv preprint arXiv:2312.05225, 2023 - arxiv.org
We present Neural Spectral Methods, a technique to solve parametric Partial Differential
Equations (PDEs), grounded in classical spectral methods. Our method uses orthogonal …
Equations (PDEs), grounded in classical spectral methods. Our method uses orthogonal …
Recurrent flow patterns as a basis for two-dimensional turbulence: Predicting statistics from structures
A dynamical systems approach to turbulence envisions the flow as a trajectory through a
high-dimensional state space [Hopf, Commun. Appl. Maths 1, 303 (1948)]. The chaotic …
high-dimensional state space [Hopf, Commun. Appl. Maths 1, 303 (1948)]. The chaotic …