AI RESEARCH
FastLSQ: Solving PDEs in One Shot via Fourier Features with Exact Analytical Derivatives
arXiv CS.LG
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ArXi:2602.10541v3 Announce Type: replace-cross We present FastLSQ, a framework for PDE solving and inverse problems built on trigonometric random Fourier features with exact analytical derivatives. Trigonometric features admit closed-form derivatives of any order in $\calO(1)$, enabling graph-free operator assembly without autodiff. Linear PDEs: one least-squares call; nonlinear: Newton--Raphson reusing analytical assembly.