arXiv subjects

Claudia Lainscsek

Publications and source records attributed to Claudia Lainscsek.

2 recordsLinked to original sources

arXiv2026

Causal Dynamic Resonance

Many dynamical systems in nature, such as brains and weather systems, are highly nonlinear and complex. Determining information flow among the components that make up these dynamical systems is challenging. If the components are the result of a common process or become synchronized, causality measures typically fail. We previously introduced Cross-Dynamical Delay Differential Analysis (CD-DDA), a nonlinear method for assessing causal influence, along with a complementary approach for dynamical similarity between time series data, Dynamical Ergodicity Delay Differential Analysis (DE-DDA). Here, we show that ``Causal Dynamic Resonance (CDR)'' further improves the false positive rejection rate by adding white noise to the data, without perturbing the underlying dynamical system. This is followed by a study of CDR in coupled Rössler systems, where ground truth interactions are known and in invasive intracranial electroencephalographic (iEEG) data from drug-resistant epilepsy patients undergoing presurgical monitoring.

nlin.CD↗

arXiv2020

Assessing observability of chaotic systems using Delay Differential Analysis

Observability can determine which recorded variables of a given system are optimal for discriminating its different states. Quantifying observability requires knowledge of the equations governing the dynamics. These equations are often unknown when experimental data are considered. Consequently, we propose an approach for numerically assessing observability using Delay Differential Analysis (DDA). Given a time series, DDA uses a delay differential equation for approximating the measured data. The lower the least squares error between the predicted and recorded data, the higher the observability. We thus rank the variables of several chaotic systems according to their corresponding least square error to assess observability. The performance of our approach is evaluated by comparison with the ranking provided by the symbolic observability coefficients as well as with two other data-based approaches using reservoir computing and singular value decomposition of the reconstructed space. We investigate the robustness of our approach against noise contamination.

nlin.AO↗