【專題演講】2024-04-11 16:10-17:00 Empirical Dynamic Modeling for nonlinear dynamical systems- Application and extension of Takens’ Embedding theorem.
數學跨領域研究中心 2024年專題演講
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DATE
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2024-04-11 16:10-17:00
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PLACE
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理學教學大樓36102室
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SPEAKER
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Professor Chih-hao Hsieh Institute of Oceanography, National Taiwan University
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TITLE
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Empirical Dynamic Modeling for nonlinear dynamical systems- Application and extension of Takens’ Embedding theorem.
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ABESTRACT
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Mechanistic understanding and forecasting are important for effective policy and management recommendations for ecosystems. However, these tasks are challenging, because real world is complex, where correlation does not necessarily imply causation. In this seminar, I aim to introduce a time-series analytical framework, known as Empirical Dynamic Modeling (EDM). EDM is rooted in Takens’ Embedding theorem for state space reconstruction. EDM enables detecting causality among interacting components in nonlinear dynamical systems, constructing time-varying interaction networks, forecasting effects of external forcing, and serving as early warning signal for critical transition. I will demonstrate the efficacy of EDM in various systems. The information can shed light on identifying drivers of ecosystem stability and translating this science into policy-relevant information.
References:
Sugihara, et al. (2012). Detecting causality in complex ecosystems. Science 338: 496-500.
Chang, et al. (2017). Empirical dynamic modeling for beginners. Ecological Research 32: 785-796.
Chang, et al. (2020). Long-term warming destabilizes aquatic ecosystems through weakening biodiversity-mediated causal networks. Global Change Biology 26: 6413-6423.
Chang, et al. (2021). Reconstructing large interaction networks from empirical time series data. Ecology Letters 24: 2763-2774.
Grziwotz, et al. (2023). Anticipating the occurrence and type of critical transitions. Science Advances 9: eabq4558.
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SPONSOR
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國立成功大學數學系、國立成功大學數學跨領域研究中心、國立成功大學理學院
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