TokaMark: New Benchmark Dataset Released for AI-Driven Tokamak Plasma Modeling
Researchers have released TokaMark, a comprehensive benchmark dataset and evaluation framework for testing artificial intelligence models on real tokamak plasma data from the MAST facility. The benchmark addresses a critical gap in fusion research by providing standardized, openly available datasets and evaluation protocols that have previously been fragmented across institutions. This resource aims to accelerate development of AI-based plasma prediction methods essential for commercially viable fusion energy reactors.
TokaMark is a structured benchmark designed to evaluate AI models on experimental data from the Mega Ampere Spherical Tokamak (MAST), addressing a major bottleneck in fusion energy research. The benchmark includes 14 standardized tasks spanning different physical mechanisms, multiple diagnostics, and various operational scenarios, along with baseline models and comprehensive tooling. The dataset and associated resources have been open-sourced to enable reproducible research and fair comparison of approaches. The release tackles a longstanding problem in the field: fusion datasets have been scarce, institution-specific, and inconsistently annotated, preventing scalable evaluation of data-driven methods. By unifying access to multi-modal fusion data and standardizing evaluation protocols, TokaMark aims to accelerate progress in AI-based plasma modeling, a critical capability for predicting plasma dynamics from the sparse and noisy sensor readings characteristic of real tokamak operations.
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- arXiv cs.AICenter
TokaMark: A Comprehensive Benchmark for MAST Tokamak Plasma Models
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