171 citations · 2.6k across the 110 of their papers we have counts for
117 papers
Compute-optimal data scaling for neural surrogates via multi-fidelity training
Paul Setinek, Fabian Paischer, Nils Thuerey +2
Neural surrogates for Partial Differential Equations (PDEs) are usually trained on synthetic data from numerical simulators. As models grow and scaling laws demand larger datasets,…
Shieldstral
Antonia Calvi, Avinash Sooriyarachchi, Giada Pistilli +273
We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7 its size on text safety benchmarks and set…
Stochastic Counterdiabatic Driving via Biorthogonal Liouvillian Eigenmodes
Sandeep Suresh Cranganore, Sebastian Lehner, Johannes Brandstetter +1
Finite-time driving of stochastic systems generates excess dissipation, causing the evolving probability distribution to lag behind the instantaneous equilibrium, and consequently…
A Shortcut to Statistically Steady-State Turbulence with Flow Matching
Gianluca Galletti, Gerald Gutenbrunner, William Hornsby +5
Many nonlinear physical systems exhibit an initial transient phase in which perturbations grow before nonlinear interactions lead to a statistically steady state. While this satura…
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…
ANTIC: Adaptive Neural Temporal In-situ Compressor
Sandeep S. Cranganore, Andrei Bodnar, Gianluca Galletti +2
The persistent storage requirements for high-resolution, spatiotemporally evolving fields governed by large-scale and high-dimensional partial differential equations (PDEs) have re…
gyaradax: Local Gyrokinetics JAX Code
Gianluca Galletti, Eric Volkmann, Johannes Brandstetter
Gyrokinetic simulations are essential for understanding and controlling turbulence in fusion plasmas, yet they are oftentimes implemented in legacy codebases, in many cases CPU-bou…
Stabilizing Test-Time Adaptation of High-Dimensional Simulation Surrogates via D-Optimal Statistics
Anna Zimmel, Paul Setinek, Gianluca Galletti +2
Machine learning surrogates are increasingly used in engineering to accelerate costly simulations, yet distribution shifts between training and deployment often cause severe perfor…
Physics-Informed Neural Compression of High-Dimensional Plasma Data
Gianluca Galletti, Gerald Gutenbrunner, Sandeep S. Cranganore +6
High-fidelity scientific simulations are now producing unprecedented amounts of data, creating a storage and analysis bottleneck. A single simulation can generate tremendous data v…
Meltdown: Circuits and Bifurcations in Point-Cloud-Conditioned 3D Diffusion Transformers
Maximilian Plattner, Fabian Paischer, Johannes Brandstetter +1
Sparse point clouds are a common input modality for 3D surface reconstruction, including in safety-critical settings such as surgical navigation and autonomous perception. Recent p…
WIND: Weather Inverse Diffusion for Zero-Shot Atmospheric Modeling
Michael Aich, Andreas Fürst, Florian Sestak +3
Deep learning has revolutionized weather forecasting, but many challenges remain, including climate modeling. Moreover, the current landscape remains fragmented: highly specialized…
Autoregressive deep learning for real-time simulation of soft tissue dynamics during virtual neurosurgery
Fabian Greifeneder, Wolfgang Fenz, Benedikt Alkin +3
Accurate simulation of brain deformation is a key component for developing realistic, interactive neurosurgical simulators, as complex nonlinear deformations must be captured to en…
Fluid Intelligence: A Forward Look on AI Foundation Models in Computational Fluid Dynamics
Neil Ashton, Johannes Brandstetter, Siddhartha Mishra
Driven by the advancement of GPUs and AI, the field of Computational Fluid Dynamics (CFD) is undergoing significant transformations. This paper bridges the gap between the machine…
Neural surrogates for designing gravitational wave detectors
Carlos Ruiz-Gonzalez, Sören Arlt, Sebastian Lehner +5
Physics simulators are essential in science and engineering, enabling the analysis, control, and design of complex systems. In experimental sciences, they are increasingly used to…
Towards Multi-Fidelity Scaling Laws of Neural Surrogates in CFD
Paul Setinek, Gianluca Galletti, Johannes Brandstetter
Scaling laws describe how model performance grows with data, parameters and compute. While large datasets can usually be collected at relatively low cost in domains such as languag…
Going with the Speed of Sound: Pushing Neural Surrogates into Highly-turbulent Transonic Regimes
Fabian Paischer, Leo Cotteleer, Yann Dreze +5
The widespread use of neural surrogates in automotive aerodynamics, enabled by datasets such as DrivAerML and DrivAerNet++, has primarily focused on bluff-body flows with large wak…
AB-UPT for Automotive and Aerospace Applications
Benedikt Alkin, Richard Kurle, Louis Serrano +2
The recently proposed Anchored-Branched Universal Physics Transformers (AB-UPT) shows strong capabilities to replicate automotive computational fluid dynamics simulations requiring…
PAINT: Parallel-in-time Neural Twins for Dynamical System Reconstruction
Andreas Radler, Vincent Seyfried, Johannes Brandstetter +1
Neural surrogates have shown great potential in simulating dynamical systems, while offering real-time capabilities. We envision Neural Twins as a progression of neural surrogates,…
GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations
Fabian Paischer, Gianluca Galletti, William Hornsby +5
Nuclear fusion plays a pivotal role in the quest for reliable and sustainable energy production. A major roadblock to viable fusion power is understanding plasma turbulence, which…
Einstein Fields: A Neural Perspective To Computational General Relativity
Sandeep Suresh Cranganore, Andrei Bodnar, Arturs Berzins +1
We introduce Einstein Fields, a neural representation designed to compress computationally intensive four-dimensional numerical relativity simulations into compact implicit neural…
SIMSHIFT: A Benchmark for Adapting Neural Surrogates to Distribution Shifts
Paul Setinek, Gianluca Galletti, Thomas Gross +3
Neural surrogates for Partial Differential Equations (PDEs) often suffer significant performance degradation when evaluated on problem configurations outside their training distrib…
Advanced Long-term Earth System Forecasting
Hao Wu, Yuan Gao, Ruijian Gou +30
Reliable long-term forecasting of Earth system dynamics is fundamentally limited by instabilities in current artificial intelligence (AI) models during extended autoregressive simu…
Towards scientific machine learning for granular material simulations -- challenges and opportunities
Marc Fransen, Andreas Fürst, Deepak Tunuguntla +21
Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights…
Shape Generation via Weight Space Learning
Maximilian Plattner, Arturs Berzins, Johannes Brandstetter
Foundation models for 3D shape generation have recently shown a remarkable capacity to encode rich geometric priors across both global and local dimensions. However, leveraging the…