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

Code

• github.com/sulcantonin/fastlsq
  OfficialIn paper★ 26

Abstract

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 (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 0.07\,s (linear) and 10^-8--10^-9 in <9\,s (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; pip install fastlsq.

Reproductions