3 citations · 7 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
Physics-embedded Fourier Neural Network for Partial Differential Equations
Qingsong Xu, Nils Thuerey, Yilei Shi +3
We consider solving complex spatiotemporal dynamical systems governed by partial differential equations (PDEs) using frequency domain-based discrete learning approaches, such as Fo…
Control of Two-way Coupled Fluid Systems with Differentiable Solvers
Brener Ramos, Felix Trost, Nils Thuerey
We investigate the use of deep neural networks to control complex nonlinear dynamical systems, specifically the movement of a rigid body immersed in a fluid. We solve the Navier St…