FastLSQ: Solving PDEs in One Shot via Fourier Features with Exact Analytical Derivatives

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 $\mathcal{O}(1)$, enabling graph-free operator assembly without autodiff. Linear PDEs: one least-squares call; nonlinear: Newton–Raphson reusing analytical assembly. On 17 PDEs (1–6D), FastLSQ achieves ${10}^{-7}$ in 0.07s (linear) and ${10}^{-8}$–${10}^{-9}$ in $<$9s (nonlinear), orders of magnitude faster and more accurate than iterative PINNs. Analytical higher-order derivatives yield a differentiable digital twin; we demonstrate inverse problems (heat-source, coil recovery) and PDE discovery. Code: github.com/sulcantonin/FastLSQ and $\mathtt{\text{pip}}\mathtt{\text{install}}\mathtt{\text{fastlsq}}$.