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05/24/2023
Learning Lagrangian Fluid Mechanics with E(3)-Equivariant Graph Neural Networks
We contribute to the vastly growing field of machine learning for engine...
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03/31/2023
E(3) Equivariant Graph Neural Networks for Particle-Based Fluid Mechanics
We contribute to the vastly growing field of machine learning for engine...
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03/31/2023
On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods
Recent developments in Machine Learning approaches for modelling physica...
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03/25/2022
JAX-FLUIDS: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows
Physical systems are governed by partial differential equations (PDEs). ...
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12/09/2021
A fully-differentiable compressible high-order computational fluid dynamics solver
Fluid flows are omnipresent in nature and engineering disciplines. The r...
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01/25/2021
Inferring incompressible two-phase flow fields from the interface motion using physics-informed neural networks
In this work, physics-informed neural networks are applied to incompress...
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10/26/2020
A low-dissipation shock-capturing framework with flexible nonlinear dissipation control
In this work, a framework to construct arbitrarily high-order low-dissip...
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04/07/2019