Data-Driven Modeling and Control of Complex Systems
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Organizer(s): |
Name:
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Affiliation:
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Country:
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Qunxi Zhu
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Fudan University
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Peoples Rep of China
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Siyang Leng
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Fudan University
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Peoples Rep of China
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Peijie Zhou
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Peking University
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Peoples Rep of China
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Introduction:
| | Complex systems manifest pervasively across diverse real-world scenarios spanning microscopic to macroscopic scales. Current research is undergoing a paradigm shift from conventional mechanism-based analysis to data-driven modeling methodologies. This endeavor necessitates developing efficient data-driven dynamical models and computational frameworks leveraging massive or limited time-series data, which demands breakthroughs in overcoming traditional algorithms` scalability limitations while preserving their capacity for characterizing nonlinear system features. By revealing intrinsic system architectures and dynamic principles, such advancements will enhance simulation fidelity and prediction accuracy of evolutionary behaviors, achieve intelligent regulation, and ultimately facilitate accelerated progress in complex systems research.
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