15 citations · 19 across the 5 of their papers we have counts for
5 papers
MGKDB: An IMAS-aligned multicode gyrokinetic simulation database for reproducible fusion turbulence modeling and data-driven analysis
Craig Michoski, David R. Hatch, Dongyang Kuang +14
Expensive fusion simulations are commonly preserved in code-specific formats that limit discovery, comparison, and reuse. We present the Multiscale GyroKinetic DataBase (MGKDB), an…
Efficient dataset construction using active learning and uncertainty-aware neural networks for plasma turbulent transport surrogate models
Aaron Ho, Lorenzo Zanisi, Bram de Leeuw +3
This work demonstrates a proof-of-principle for using uncertainty-aware architectures, in combination with active learning techniques and an in-the-loop physics simulation code as…
Duqtools: Dynamic uncertainty quantification for Tokamak reactor simulations modelling
Victor Azizi, Stef Smeets, Florian Koechl +3
Large scale validation and uncertainty quantification are essential in the experimental design, control, and operations of fusion reactors. Reduced models and increasing computatio…
Evaluating Language-Model Agents on Realistic Autonomous Tasks
Megan Kinniment, Lucas Jun Koba Sato, Haoxing Du +10
In this report, we explore the ability of language model agents to acquire resources, create copies of themselves, and adapt to novel challenges they encounter in the wild. We refe…
Efficient training sets for surrogate models of tokamak turbulence with Active Deep Ensembles
L. Zanisi, A. Ho, T. Madula +7
Model-based plasma scenario development lies at the heart of the design and operation of future fusion powerplants. Including turbulent transport in integrated models is essential…