@AnimaAnandkumar: This is something I have been emphasizing since we started our work on Neural Operators. We very quickly went from simp…
Summary
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.
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Cached at: 06/11/26, 01:59 PM
This is something I have been emphasizing since we started our work on Neural Operators. We very quickly went from simple fluid dynamics benchmarks to hard problems like building the first high-resolution AI-weather model, FourCastNet, and modeling turbulence in nuclear fusion. For those applications, we got speedup of 10,000 - million times. Simple benchmarks are great to test new architecture/algorithms work, but not the end.
Yijing Zhang (@YijingZ91217): Neural PDE solvers have seen exciting progress! 🌊 But despite growing adoption, we still don’t know 𝘄𝗵𝗲𝗻 we should use them instead of classical solvers. 🤔 Our new paper has a surprising finding: the harder the PDE task, the more cost-effective learned solvers become. 🧵👇
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