Comparing Deterministic and Stochastic Parameter Recovery Algorithms Applied to Chaotic Systems

2026/06/17 by Ashley Wang, Elizabeth Carlson, Franca Hoffmann · 1 voice
Physics and Astronomy · Mathematics · #nlin.CD #math.OC

paper · pdf

Abstract

This paper explores the effectiveness of various novel deterministic and traditional stochastic data assimilation (DA) and parameter recovery (PR) algorithms given noisy data from chaotic systems. We use semi-analytic methods to numerically construct synthetic data from the Lorenz '63 and multiscale Lorenz '96 chaotic dynamical systems, adding white noise. Our findings show that, for different noise levels, deterministic PR algorithms paired with deterministic DA algorithms are shown computationally to be overall more accurate and stable than stochastic PR algorithms. Additionally, deterministic PR methods have demonstrated greater speed and efficiency, requiring less computational power than stochastic PR methods. This suggests that future work should consider exploring the full potential of deterministic PR algorithms in the presence of noise.

Citations

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Discussions

• Ashley Wang, Elizabeth Carlson, Franca Hoffman: Comparing Deterministic and Stochastic Parameter Recovery Algorithms Applied to Chaotic Systems https://arxiv.org/abs/2606.18568 https://arxiv.org/pdf/2 [bsky, 0 points, 0 comments]

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