12 citations · 14 across the 7 of their papers we have counts for
7 papers
On the Generalization in Topology Optimization via Sensitivity-Conditioned Bernoulli Flow Matching
Mohammad Rashed, Duarte F. Valoroso Madeira, Babak Gholami +3
Surrogate models for topology optimization (TO) exhibit highly variable out-of-distribution (OOD) generalization under distribution shifts such as changing loads or boundary condit…
One Scale at a Time: Scale-Autoregressive Modeling for Fluid Flow Distributions
Mario Lino, Nils Thuerey
Analyzing unsteady fluid flows often requires access to the full distribution of possible temporal states, yet conventional PDE solvers are computationally prohibitive and learned…
Bias-Constrained Diffusion Schedules for PDE Emulations: Reconstruction Error Minimization and Efficient Unrolled Training
Constantin Le Cleï, Nils Thuerey, Xiaoxiang Zhu
Conditional Diffusion Models are powerful surrogates for emulating complex spatiotemporal dynamics, yet they often fail to match the accuracy of deterministic neural emulators for…
Physics-Constrained Adaptive Flow Matching for Climate Downscaling
Kevin Debeire, Aytaç Paçal, Pierre Gentine +3
Regional climate information at kilometer scales is essential for assessing the impacts of climate change, but generating it with global climate models is too expensive due to thei…
ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks
Qiang Liu, Mengyu Chu, Nils Thuerey
The loss functions of many learning problems contain multiple additive terms that can disagree and yield conflicting update directions. For Physics-Informed Neural Networks (PINNs)…
Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics
Lukas Prantl, Benjamin Ummenhofer, Vladlen Koltun +1
We present a novel method for guaranteeing linear momentum in learned physics simulations. Unlike existing methods, we enforce conservation of momentum with a hard constraint, whic…
Wavelet-based Loss for High-frequency Interface Dynamics
Lukas Prantl, Jan Bender, Tassilo Kugelstadt +1
Generating highly detailed, complex data is a long-standing and frequently considered problem in the machine learning field. However, developing detail-aware generators remains an…