39 citations · 61 across the 15 of their papers we have counts for
4 papers · 1 filter
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…
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…