From the 1 of 10 linked papers with an AI index.
10 papers
A Shortcut to Statistically Steady-State Turbulence with Flow Matching
Gianluca Galletti, Gerald Gutenbrunner, William Hornsby +5
The paper presents GyroFlow, a latent generative model that directly creates statistically steady‑state snapshots of gyrokinetic turbulence, avoiding the costly transient simulatio…
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…
Data-Efficient Neural Operator Training via Physics-Based Active Learning
Alicja Polanska, Lorenzo Zanisi, Vignesh Gopakumar +1
Solving partial differential equations with neural operators significantly reduces computational costs but remains bottlenecked by high training data requirements. Active learning…
Learning Physical Operators using Neural Operators
Vignesh Gopakumar, Ander Gray, Dan Giles +5
Neural operators have emerged as promising surrogate models for solving partial differential equations (PDEs), but struggle to generalise beyond training distributions and are ofte…
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…
Uncertainty Quantification of Surrogate Models using Conformal Prediction
Vignesh Gopakumar, Ander Gray, Joel Oskarsson +5
Data-driven surrogate models offer quick approximations to complex numerical and experimental systems but typically lack uncertainty quantification, limiting their reliability in s…