Approximating the linear response of physical chaos

Journal Article · 26 September 2022 · Nonlinear Dynamics

DOI: https://doi.org/10.1007/s11071-022-07885-7 · OSTI ID:1889783

Śliwiak, Adam A. ; Wang, Qiqi

Abstract Parametric derivatives of statistics are highly desired quantities in prediction, design optimization and uncertainty quantification. In the presence of chaos, the rigorous computation of these quantities is certainly possible, but mathematically complicated and computationally expensive. Based on Ruelle’s formalism, this paper shows that the sophisticated linear response algorithm can be dramatically simplified in higher-dimensional systems featuring statistical homogeneity in the physical space. We argue that the contribution of the SRB (Sinai–Ruelle–Bowen) measure gradient, which is an integral yet the most cumbersome part of the full algorithm, is negligible if the objective function is appropriately aligned with unstable manifolds. This abstract condition could potentially be satisfied by a vast family of real-world chaotic systems, regardless of the physical meaning and mathematical form of the objective function and perturbed parameter. We demonstrate several numerical examples that support these conclusions and that present the use and performance of a simplified linear response algorithm. In the numerical experiments, we consider physical models described by differential equations, including Lorenz 96 and Kuramoto–Sivashinsky.

• Research Organization: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: NA0003993
• OSTI ID: 1889783
• Journal Information: Nonlinear Dynamics, Journal Name: Nonlinear Dynamics Journal Issue: 2 Vol. 111; ISSN 0924-090X
• Publisher: Springer Science + Business MediaCopyright Statement
• Country of Publication: Netherlands
• Language: English