Presentation Information
[MS16-02]Reservoir computing-driven causal discovery in complex systems
*Siyang Leng1 (1. Fudan University (China))
Keywords:
Dynamical causality,Reservoir computing
Causality reflects the intrinsic mechanisms and core interaction principles underlying the evolution of complex systems. Existing causal discovery methods usually encounter ethical or technical constraints in interventional experiments. In recent years, the development of AI techniques such as reservoir computing has brought novel tools for modeling and predicting the evolution of complex systems, while also offering fresh perspectives for data-driven causal discovery. This talk introduces recent theoretical advancements and integration approaches for causal discovery and reservoir computing, and discusses how AI techniques can be employed to construct digital twins of evolving complex systems, thereby enabling the discovery of interventional dynamical causality.