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cs.AI2026
AutoPDE: Reliable Agentic PDE Solving via Explicitly Represented Solver Strategies
Huanshuo Dong, Keyao Zhang, Hong Wang +6
Numerical solvers for partial differential equations (PDEs) are core computational tools in science and engineering. Building reliable PDE solvers requires not only executable code…
cs.LG2026
AOT-POT: Adaptive Operator Transformation for Large-Scale PDE Pre-training
Qitan Lv, Hong Wang, Zhongkai Hao +5
Pre-training neural operators on diverse partial differential equation (PDE) datasets has emerged as a promising direction for building general-purpose surrogate models in scientif…
cs.LG2026
DSO: Dual-Scale Neural Operators for Stable Long-term Fluid Dynamics Forecasting
Huanshuo Dong, Hao Wu, Hong Wang +2
Long-term fluid dynamics forecasting is a critically important problem in science and engineering. While neural operators have emerged as a promising paradigm for modeling systems…