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Found 10 papers, 3 papers with code
Machine Learning Enhanced Hankel Dynamic-Mode Decomposition
This appears to be a key feature in enhancing the DMD overall, and it should help provide further insight for developing other deep learning methods for time series analysis and model generation.
Next Generation Reservoir Computing
Reservoir computing is a best-in-class machine learning algorithm for processing information generated by dynamical systems using observed time-series data.
Emergent hierarchy through conductance-based node constraints
The presence of hierarchy in many real-world networks is not yet fully explained.
Physics and Society
Melnikov theory for two-dimensional manifolds in three-dimensional flows
In unperturbed situations with a two-dimensional heteroclinic manifold, we adapt our theory to quantify the splitting into a stable and unstable manifold, and thereby obtain a Melnikov function characterizing the time-varying locations of transverse intersections of these manifolds.
Dynamical Systems
Entropic Causal Inference for Neurological Applications
The ultimate goal of cognitive neuroscience is to understand the mechanistic neural processes underlying the functional organization of the brain.
ERFit: Entropic Regression Fit Matlab Package, for Data-Driven System Identification of Underlying Dynamic Equations
Data-driven sparse system identification becomes the general framework for a wide range of problems in science and engineering.
Data-Driven Learning of Boolean Networks and Functions by Optimal Causation Entropy Principle (BoCSE)
Automated learning of a Boolean network and Boolean functions, from data, is a challenging task due in part to the large number of unknowns (including both the structure of the network and the functions) to be estimated, for which a brute force approach would be exponentially complex.
An Early Warning Sign of Critical Transition in The Antarctic Ice Sheet -- A Data Driven Tool for Spatiotemporal Tipping Point
Our recently developed tool, called Directed Affinity Segmentation was originally designed for data-driven discovery of coherent sets in fluidic systems.
Geometric Considerations of a Good Dictionary for Koopman Analysis of Dynamical Systems: Cardinality, 'Primary Eigenfunction,' and Efficient Representation
Representation of a dynamical system in terms of simplifying modes is a central premise of reduced order modelling and a primary concern of the increasingly popular DMD (dynamic mode decomposition) empirical interpretation of Koopman operator analysis of complex systems.
Bayesian Optical Flow with Uncertainty Quantification
Optical flow refers to the visual motion observed between two consecutive images.
