4 citations · 6 across the 4 of their papers we have counts for
6 papers
Per Astronomix ad Astra: High-Order Differentiable (Magneto)hydrodynamics with Energy-Conserving Self-Gravity
Leonard Storcks, Nils Thuerey, Tobias Buck
We present astronomix, a performant differentiable (magneto)hydrodynamics simulator written in Python/JAX. We demonstrate how automatic differentiation, validated against hand-deri…
INC: An Indirect Neural Corrector for Auto-Regressive Hybrid PDE Solvers
Hao Wei, Aleksandra Franz, Bjoern List +1
When simulating partial differential equations, hybrid solvers combine coarse numerical solvers with learned correctors. They promise accelerated simulations while adhering to phys…
Deep learning-based predictive modelling of transonic flow over an aerofoil
Li-Wei Chen, Nils Thuerey
Effectively predicting transonic unsteady flow over an aerofoil poses inherent challenges. In this study, we harness the power of deep neural network (DNN) models using the attenti…
Differentiability in Unrolled Training of Neural Physics Simulators on Transient Dynamics
Bjoern List, Li-Wei Chen, Kartik Bali +1
Unrolling training trajectories over time strongly influences the inference accuracy of neural network-augmented physics simulators. We analyze this in three variants of training n…
Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation
Georg Kohl, Li-Wei Chen, Nils Thuerey
Simulating turbulent flows is crucial for a wide range of applications, and machine learning-based solvers are gaining increasing relevance. However, achieving temporal stability w…
Towards high-accuracy deep learning inference of compressible turbulent flows over aerofoils
Li-Wei Chen, Nils Thuerey
The present study investigates the accurate inference of Reynolds-averaged Navier-Stokes solutions for the compressible flow over aerofoils in two dimensions with a deep neural net…