Chaos control and synchronization

Forward time-series causal inference based on forest weights and conditional dependence

This paper introduces F-CODEC, a causal discovery method that reduces false positives in nonlinear dynamical systems without surrogate data, validated on ecological time series.

Forest-weighted non-linear causal inference for high-dimensional and multimodal time series

This paper introduces FORWS and FORWC methods for causal inference in high-dimensional time series, showing improved forecasting and noise resilience using forest weights instead of Euclidean metrics.

Predictability failure in glucose-insulin system for ICU patients

The paper shows that pulsatile insulin and step-wise glucose infusion in ICU patients can induce sustained chaos in the glucose-insulin system, limiting predictability of physiological responses.

Extending empirical dynamic modeling to cross-sectional data beyond traditional time series.

This paper extends empirical dynamic modeling to cross-sectional data by using random data projection and validates it on simulated data.