AI RESEARCH
Active Learning with Selective Time-Step Acquisition for PDEs
arXiv CS.LG
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ArXi:2511.18107v2 Announce Type: replace Accurately solving partial differential equations (PDEs) is critical to understanding complex scientific and engineering phenomena, yet traditional numerical solvers are computationally expensive. Surrogate models offer a efficient alternative, but their development is hindered by the cost of generating sufficient