SDE decomposition and A-type stochastic interpretation in nonequilibrium processes

Yuan, Ruoshi; Tang, Ying; Ao, Ping

doi:10.1007/s11467-017-0718-2

Cited by 17 publications

(22 citation statements)

References 35 publications

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“…For instance, Kwon et al proposed a stochastic dynamic decomposition method to analyze the dynamics near stable or unstable states. This modeling approach can generate the landscape and the associated energy function, considering the inherent asymmetry of biological networks (Kwon et al, 2005;Yuan et al, 2017a). However, this approach is based on stochastic differential equations, and requires parameters related to each interaction.…”

Section: Discussionmentioning

confidence: 99%

Modeling Basins of Attraction for Breast Cancer Using Hopfield Networks

et al. 2020

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Section: Discussionmentioning

confidence: 99%

Modeling Basins of Attraction for Breast Cancer Using Hopfield Networks

et al. 2020

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“…It is possible to write the drift vector field as the sum of two vector fields: Fifalse(boldxfalse)=Rifalse(boldxfalse)−Cijfalse(boldxfalse)∂jΦfalse(boldxfalse),1emRifalse(boldxfalse)∂iΦfalse(boldxfalse)=0. A different strategy for attaining the decomposition of the drift term into a symmetric and an antisymmetric component has been proposed by Ao [89] and Yuan et al . [90].…”

Section: Qualitative and Quantitative Aspects Of The Multistability Of The Climate Systemmentioning

confidence: 99%

“…A different strategy for attaining the decomposition of the drift term into a symmetric and an antisymmetric component has been proposed by Ao [89] and Yuan et al [90].…”

Section: (B) Outline Of the Papermentioning

confidence: 99%

Dynamical landscape and multistability of a climate model

et al. 2021

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We apply two independent data analysis methodologies to locate stable climate states in an intermediate complexity climate model and analyse their interplay. First, drawing from the theory of quasi-potentials, and viewing the state space as an energy landscape with valleys and mountain ridges, we infer the relative likelihood of the identified multistable climate states and investigate the most likely transition trajectories as well as the expected transition times between them. Second, harnessing techniques from data science, and specifically manifold learning, we characterize the data landscape of the simulation output to find climate states and basin boundaries within a fully agnostic and unsupervised framework. Both approaches show remarkable agreement, and reveal, apart from the well known warm and snowball earth states, a third intermediate stable state in one of the two versions of PLASIM, the climate model used in this study. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production via the hydrological cycle drastically change the topography of the dynamical landscape of Earth’s climate.

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“…, J , see [116] and the careful description recently provided by [287]. A separate view on this problem, based upon a different interpretation of the noise has been proposed in [8,284].…”

Section: Metastability and Noise-induce Transitions Across Melancholia Statesmentioning

confidence: 99%

Applications of large deviation theory in geophysical fluid dynamics and climate science

et al. 2021

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The climate is a complex, chaotic system with many degrees of freedom. Attaining a deeper level of understanding of climate dynamics is an urgent scientific challenge, given the evolving climate crisis. In statistical physics, many-particle systems are studied using Large Deviation Theory (LDT). A great potential exists for applying LDT to problems in geophysical fluid dynamics and climate science. In particular, LDT allows for understanding the properties of persistent deviations of climatic fields from long-term averages and for associating them to low-frequency, large-scale patterns. Additionally, LDT can be used in conjunction with rare event algorithms to explore rarely visited regions of the phase space. These applications are of key importance to improve our understanding of high-impact weather and climate events. Furthermore, LDT provides tools for evaluating the probability of noise-induced transitions between metastable climate states. This is, in turn, essential for understanding the global stability properties of the system. The goal of this review is manifold. First, we provide an introduction to LDT. We then present the existing literature. Finally, we propose possible lines of future investigations. We hope that this paper will prepare the ground for studies applying LDT to solve problems encountered in climate science and geophysical fluid dynamics.

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SDE decomposition and A-type stochastic interpretation in nonequilibrium processes

Cited by 17 publications

References 35 publications

Modeling Basins of Attraction for Breast Cancer Using Hopfield Networks

Modeling Basins of Attraction for Breast Cancer Using Hopfield Networks

Dynamical landscape and multistability of a climate model

Applications of large deviation theory in geophysical fluid dynamics and climate science

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