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Data Driven Interdisciplinary Science

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Data Driven Interdisciplinary Science

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Research Frontiers in Dynamical Systems Inference from Time Series

Reconstructs differential equations and state-space models governing complex systems using sparse identification, neural differential equations, and Bayesian inference.

Reconstructing Hidden States from Partial Observations

Causal Inference in High-Dimensional Temporal Networks

Nonlinear Dynamics Discovery Without Explicit Models

Tipping Points and Early Warning Signals Detection

Sparse Identification of Dynamical Systems Architecture

Multiscale Temporal Patterns in Complex Systems

Machine Learning Interpretability for Dynamical Discovery

Synchronization and Coupling Inference Across Domains

Forecasting Beyond Linear Assumptions in Chaotic Systems

Latent Dynamics and Manifold Learning from Noisy Data

All Data-Driven Interdisciplinary Science PhD categories