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Found 12 papers, 6 papers with code
JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework
Particle-based fluid simulations have emerged as a powerful tool for solving the Navier-Stokes equations, especially in cases that include intricate physics and free surfaces.
Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics
Smoothed particle hydrodynamics (SPH) is omnipresent in modern engineering and scientific disciplines.
JAX-Fluids 2.0: Towards HPC for Differentiable CFD of Compressible Two-phase Flows
In our effort to facilitate machine learning-assisted computational fluid dynamics (CFD), we introduce the second iteration of JAX-Fluids.
LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite
Machine learning has been successfully applied to grid-based PDE modeling in various scientific applications.
Learning Lagrangian Fluid Mechanics with E($3$)-Equivariant Graph Neural Networks
We contribute to the vastly growing field of machine learning for engineering systems by demonstrating that equivariant graph neural networks have the potential to learn more accurate dynamic-interaction models than their non-equivariant counterparts.
E($3$) Equivariant Graph Neural Networks for Particle-Based Fluid Mechanics
We contribute to the vastly growing field of machine learning for engineering systems by demonstrating that equivariant graph neural networks have the potential to learn more accurate dynamic-interaction models than their non-equivariant counterparts.
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 physical systems have begun to mirror the past development of numerical methods in the computational sciences.
JAX-FLUIDS: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows
AD in particular is essential to ML-CFD research as it provides gradient information and enables optimization of preexisting and novel CFD models.
A fully-differentiable compressible high-order computational fluid dynamics solver
While prior work has explored differentiable algorithms for one- or two-dimensional incompressible fluid flows, we present a fully-differentiable three-dimensional framework for the computation of compressible fluid flows using high-order state-of-the-art numerical methods.
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 incompressible two-phase flow problems.
Near-surface dynamics of a gas bubble collapsing above a crevice
The impact of a collapsing gas bubble above rigid, notched walls is considered.
Fluid Dynamics
Sparse Identification of Truncation Errors
We augment a sparse regression-rooted approach with appropriate preconditioning routines to aid in the identification of the individual modified differential equation terms.
Numerical Analysis 62J05, 65F08 (Primary) 90C31, 35Q35, 68W40 (Secondary) G.1.8; G.3; F.2.0
