Predictability of Stochastic Dynamical System: A Probabilistic Perspective

Xu, Tao; He, Jianping

doi:10.1109/cdc51059.2022.9992431

Cited by 2 publications

(1 citation statement)

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“…Although predictability is challenging for stochastic systems, , the resulting correlation values between the measured and predicted values of the remaining time to the first assembly manifest a predictive power of the SLM. Inevitably, in some cases, the SLM prediction is limited, as can be seen in Figure , for larger times.…”

Section: Discussionmentioning

confidence: 99%

Nonequilibrium Self-Assembly Time Forecasting by the Stochastic Landscape Method

Faran

Bisker

2023

J. Phys. Chem. B

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Many biological systems rely on the ability to self-assemble target structures from different molecular building blocks using nonequilibrium drives, stemming, for example, from chemical potential gradients. The complex interactions between the different components give rise to a rugged energy landscape with a plethora of local minima on the dynamic pathway to the target assembly. Exploring a toy physical model of multicomponents nonequilibrium self-assembly, we demonstrate that a segmented description of the system dynamics can be used to provide predictions of the first assembly times. We show that for a wide range of values of the nonequilibrium drive, a log-normal distribution emerges for the first assembly time statistics. Based on data segmentation by a Bayesian estimator of abrupt changes (BEAST), we further present a general data-based algorithmic scheme, namely, the stochastic landscape method (SLM), for assembly time predictions. We demonstrate that this scheme can be implemented for the first assembly time forecast during a nonequilibrium self-assembly process, with improved prediction power compared to a naïve guess based on the mean remaining time to the first assembly. Our results can be used to establish a general quantitative framework for nonequilibrium systems and to improve control protocols of nonequilibrium self-assembly processes.

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Section: Discussionmentioning

confidence: 99%