2 citations · 5 across the 6 of their papers we have counts for
6 papers · 1 filter
Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows
Harish Ramachandran, Björn Kimpel, Thomas Paula +3
Compressible multiphase flows involving shocks and material interfaces arise in applications such as bubble collapse and droplet breakup, where strong nonlinear interactions produc…
Hybrid Fourier Neural Operator-Lattice Boltzmann Method
Alexandra Junk, Josef M. Winter, Meike Tütken +2
We propose an accelerated computational fluid dynamics framework based on a hybrid Fourier Neural Operator-Lattice Boltzmann Method (FNO-LBM) for steady and unsteady weakly compres…
Data-driven shape inference in three-dimensional steady state supersonic flows using ODIL and JAX-Fluids
Aaron B. Buhendwa, Deniz A. Bezgin, Petr Karnakov +2
We present a novel data- and first-principles-driven method for inferring the shape of a solid obstacle and its flow field in three-dimensional steady-state supersonic flows. The m…
JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework
Artur P. Toshev, Harish Ramachandran, Jonas A. Erbesdobler +3
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. The…
JAX-Fluids 2.0: Towards HPC for Differentiable CFD of Compressible Two-phase Flows
Deniz A. Bezgin, Aaron B. Buhendwa, Nikolaus A. Adams
In our effort to facilitate machine learning-assisted computational fluid dynamics (CFD), we introduce the second iteration of JAX-Fluids. JAX-Fluids is a Python-based fully-differ…
Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics
Artur P. Toshev, Jonas A. Erbesdobler, Nikolaus A. Adams +1
Smoothed particle hydrodynamics (SPH) is omnipresent in modern engineering and scientific disciplines. SPH is a class of Lagrangian schemes that discretize fluid dynamics via finit…