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Found 4 papers, 1 papers with code
Control of complex systems with generalized embedding and empirical dynamic modeling
We demonstrate that generalized state space embedding and prediction of model dynamics within the state space provide a data-driven process model for control of complex systems and a new paradigm of model predictive control.
Experimentally testable whole brain manifolds that recapitulate behavior
Thus, as a final validation of how well GMN captures essential dynamic information, we show that the artificially generated time series can be used as a training set to predict out-of-sample observed fly locomotion, as well as brain activity in out of sample withheld data not used in model building.
Empirical Mode Modeling: A data-driven approach to recover and forecast nonlinear dynamics from noisy data
Data-driven, model-free analytics are natural choices for discovery and forecasting of complex, nonlinear systems.
Massively Parallel Causal Inference of Whole Brain Dynamics at Single Neuron Resolution
Empirical Dynamic Modeling (EDM) is a nonlinear time series causal inference framework.
