1 citations · 1 across the 2 of their papers we have counts for
4 papers
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…
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…