6 papers
Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control
Jannis Becktepe, Aleksandra Franz, Nils Thuerey +1
Reinforcement learning (RL) has shown promising results in active flow control (AFC), yet progress in the field remains difficult to assess as existing studies rely on heterogeneou…
Towards a Foundation-Model Paradigm for Aerodynamic Prediction in Three-dimensional Design
Yunjia Yang, Babak Gholami, Caglar Gurbuz +2
Accurate machine-learning models for aerodynamic prediction are essential for accelerating shape optimization, yet remain challenging to develop for complex three-dimensional confi…
Physically consistent and uncertainty-aware learning of spatiotemporal dynamics
Qingsong Xu, Jonathan L Bamber, Nils Thuerey +5
Accurate long-term forecasting of spatiotemporal dynamics remains a fundamental challenge across scientific and engineering domains. Existing machine learning methods often neglect…
PICT -- A Differentiable, GPU-Accelerated Multi-Block PISO Solver for Simulation-Coupled Learning Tasks in Fluid Dynamics
Aleksandra Franz, Hao Wei, Luca Guastoni +1
Despite decades of advancements, the simulation of fluids remains one of the most challenging areas of in scientific computing. Supported by the necessity of gradient information i…
diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning
Rene Winchenbach, Nils Thuerey
We present diffSPH, a novel open-source differentiable Smoothed Particle Hydrodynamics (SPH) framework developed entirely in PyTorch with GPU acceleration. diffSPH is designed cent…
Component-Based Machine Learning for Indoor Flow and Temperature Fields Prediction Latent Feature Aggregation and Flow Interaction
Shaofan Wang, Nils Thuerey, Philipp Geyer
Accurate and efficient prediction of indoor airflow and temperature distributions is essential for building energy optimization and occupant comfort control. However, traditional C…