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Papers
2002.02521
Cited By

Enhancement of shock-capturing methods via machine learning

Enhancement of shock-capturing methods via machine learning

Theoretical and Computational Fluid Dynamics (TCFD), 2020

6 February 2020

ArXiv (abs)PDF HTML

Papers citing "Enhancement of shock-capturing methods via machine learning"

13 / 13 papers shown

Data-Driven Adaptive Gradient Recovery for Unstructured Finite Volume Computations

Data-Driven Adaptive Gradient Recovery for Unstructured Finite Volume Computations

G. de Romémont

118

0

0

22 Jul 2025

Rational-WENO: A lightweight, physically-consistent three-point weighted
essentially non-oscillatory scheme

Rational-WENO: A lightweight, physically-consistent three-point weighted essentially non-oscillatory scheme

Deniz A. Bezgin

Aaron B. Buhendwa

Steffen J. Schmidt

...

Spencer H. Bryngelson

Leonardo Zepeda-Núñez

276

2

0

13 Sep 2024

Weak baselines and reporting biases lead to overoptimism in machine
learning for fluid-related partial differential equations

Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations

270

117

0

09 Jul 2024

Learning WENO for entropy stable schemes to solve conservation laws

Learning WENO for entropy stable schemes to solve conservation laws

275

1

0

21 Mar 2024

Deep smoothness WENO scheme for two-dimensional hyperbolic conservation
laws: A deep learning approach for learning smoothness indicators

Deep smoothness WENO scheme for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators

Tatiana Kossaczká

Ameya Dilip Jagtap

Matthias Ehrhardt

182

2

0

18 Sep 2023

An unsupervised machine-learning-based shock sensor for high-order
supersonic flow solvers

An unsupervised machine-learning-based shock sensor for high-order supersonic flow solvers

A. Mateo-Gabín

320

0

0

28 Jul 2023

Machine Learning for Partial Differential Equations

Machine Learning for Partial Differential Equations

Steven L. Brunton

228

22

0

30 Mar 2023

Invariant preservation in machine learned PDE solvers via error
correction

Invariant preservation in machine learned PDE solvers via error correction

332

9

0

28 Mar 2023

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in
Scientific Computing

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing

263

70

0

14 Nov 2022

JAX-FLUIDS: A fully-differentiable high-order computational fluid
dynamics solver for compressible two-phase flows

JAX-FLUIDS: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flowsComputer Physics Communications (CPC), 2022

Deniz A. Bezgin

Aaron B. Buhendwa

Nikolaus A. Adams

418

103

0

25 Mar 2022

A fully-differentiable compressible high-order computational fluid
dynamics solver

A fully-differentiable compressible high-order computational fluid dynamics solver

Deniz A. Bezgin

Aaron B. Buhendwa

Nikolaus A. Adams

151

3

0

09 Dec 2021

Enhancing Computational Fluid Dynamics with Machine Learning

Enhancing Computational Fluid Dynamics with Machine Learning

Ricardo Vinuesa

Steven L. Brunton

432

499

0

05 Oct 2021

Applying Machine Learning to Study Fluid Mechanics

Applying Machine Learning to Study Fluid Mechanics

Steven L. Brunton

154

116

0

05 Oct 2021

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