120 citations · 142 across the 10 of their papers we have counts for
10 papers
A fully GPU-based workflow for building physics emulators of hypersonic flows
Fabian Paischer, Dylan Rubini, Deniz A. Bezgin +6
The ability to resolve complex physical phenomena with high fidelity and at low computational cost is central to addressing key challenges in modern engineering. A prime example li…
The HydroGym Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, Sajeda Mokbel, Miro Gondrum +18
Modeling and controlling fluids is critical across science and engineering. Effective flow control can increase lift, reduce drag, enhance mixing, and attenuate noise, potentially…
Rational-WENO: A lightweight, physically-consistent three-point weighted essentially non-oscillatory scheme
Shantanu Shahane, Sheide Chammas, Deniz A. Bezgin +8
Conventional WENO3 methods are known to be highly dissipative at lower resolutions, introducing significant errors in the pre-asymptotic regime. In this paper, we employ a rational…
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-based differentiable fluid dynamics on GPU and end-to-end optimization
Wenkang Wang, Xuanwei Zhang, Deniz Bezgin +3
This project aims to advance differentiable fluid dynamics for hypersonic coupled flow over porous media, demonstrating the potential of automatic differentiation (AD)-based optimi…
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…
JAX-FLUIDS: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows
Deniz A. Bezgin, Aaron B. Buhendwa, Nikolaus A. Adams
Physical systems are governed by partial differential equations (PDEs). The Navier-Stokes equations describe fluid flows and are representative of nonlinear physical systems with c…
A fully-differentiable compressible high-order computational fluid dynamics solver
Deniz A. Bezgin, Aaron B. Buhendwa, Nikolaus A. Adams
Fluid flows are omnipresent in nature and engineering disciplines. The reliable computation of fluids has been a long-lasting challenge due to nonlinear interactions over multiple…
Consistent and symmetry preserving data-driven interface reconstruction for the level-set method
Aaron B. Buhendwa, Deniz A. Bezgin, Nikolaus Adams
Recently, machine learning has been used to substitute parts of conventional computational fluid dynamics, e.g. the cell-face reconstruction in finite-volume solvers or the curvatu…
Inferring incompressible two-phase flow fields from the interface motion using physics-informed neural networks
Aaron B. Buhendwa, Stefan Adami, Nikolaus A. Adams
In this work, physics-informed neural networks are applied to incompressible two-phase flow problems. We investigate the forward problem, where the governing equations are solved f…