A weak-form regression framework using spatial Gaussian kernels removes bias in recovering drift b(x) and diffusion a(x) for stochastic generators from single sparse regressions, validated on benchmarks with low coefficient and density errors.
Deep learning for universal linear embeddings of nonlinear dynamics
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Weak-Form Recovery of Stochastic Generators and Dynamical Invariants
A weak-form regression framework using spatial Gaussian kernels removes bias in recovering drift b(x) and diffusion a(x) for stochastic generators from single sparse regressions, validated on benchmarks with low coefficient and density errors.