1 citations · 2 across the 11 of their papers we have counts for
12 papers
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…
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…
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…
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…
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…
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…
5D Neural Surrogates for Nonlinear Gyrokinetic Simulations of Plasma Turbulence
Gianluca Galletti, Fabian Paischer, Paul Setinek +5
Nuclear fusion plays a pivotal role in the quest for reliable and sustainable energy production. A major roadblock to achieving commercially viable fusion power is understanding pl…
Neural operator surrogate models of plasma edge simulations: feasibility and data efficiency
N. Carey, L. Zanisi, S. Pamela +7
The inclusion of high-fidelity simulations of SOL turbulence and transient MHD events such as ELMs in highly iterative applications remains computationally prohibitive, limiting th…
JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework
Artur P. Toshev, Harish Ramachandran, Jonas A. Erbesdobler +3
Particle-based fluid simulations have emerged as a powerful tool for solving the Navier-Stokes equations, especially in cases that include intricate physics and free surfaces. The…
LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite
Artur P. Toshev, Gianluca Galletti, Fabian Fritz +2
Machine learning has been successfully applied to grid-based PDE modeling in various scientific applications. However, learned PDE solvers based on Lagrangian particle discretizati…