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Anima Anandkumar highlights that neural operators, despite simple benchmarks, have achieved massive speedups (10,000–million times) in hard real-world problems like high-resolution AI weather modeling (FourCastNet) and nuclear fusion turbulence, referencing a new paper showing learned solvers become more cost-effective as PDE tasks get harder.
This paper introduces RL4F, an offline reinforcement learning benchmark for plasma control in nuclear fusion, providing closed-loop evaluation environments and baseline comparisons across four profile tracking tasks using real tokamak data from DIII-D. The codebase and datasets are open-sourced to foster further research.