The Doctoral School in Sciences and Engineering is happy to invite you to Aoran WANG’s defence entitled
Structural Inference of Interacting Dynamical Systems
Supervisor: Prof. Jun PANG
This thesis delves into advanced methodologies for structural inference in dynamical systems, particularly focusing on the challenge of deducing underlying interaction graphs from observable data. The research encapsulates six seminal papers that collectively push the boundaries of iterative optimization, deep active learning, reservoir computing, partial correlation coefficients, and state-space models.
At the core of the contributions is a novel iterative structural inference model utilizing variational autoencoders. This model systematically refines interactions, enhancing directional accuracy and incorporating regularization for better complex systems modeling. Additionally, a deep active learning framework is introduced. It leverages neural networks to boost inference accuracy with minimal prior knowledge, demonstrating scalability and superior performance across large-scale systems.
This work also includes a robust benchmarking of structural inference methods, showcasing the efficacy of integrating reservoir computing to capture interactions within high-dimensional data contexts. This integration proves particularly effective in handling sparse data scenarios. Furthermore, the application of partial correlation coefficients offers a statistical technique to pinpoint direct interactions, facilitating scalability. The incorporation of state-space models addresses the challenges posed by irregularly observed trajectories and incomplete observations, enhancing the robustness of our approach.
Extensive evaluations across simulated and real-world datasets confirm the scalability, precision, and robustness of these methodologies, establishing a new benchmark in the field of structural inference.