53 citations · 74 across the 12 of their papers we have counts for
12 papers
Scale Partitioning by Incremental Nested Entropy: A Measure-Oriented Theory of Multiscale Structure
Abd AlRahman R. AlMomani
Across complex systems science, networks, spatial structures, populations, spectra, and dynamical flows are often represented by object-level measures such as connectivity, frequen…
Structural Causal Discovery and Predictive Sufficiency in High-Dimensional Dynamical Systems
Abd AlRahman R. AlMomani, Curtis N. James, Christopher C. Hennon +1
High-dimensional environmental systems contain variables that may be predictive, causally informative, physically coupled, or redundant, and these roles need not coincide. We study…
Boltzmann-Shannon Index: A Geometric-Aware Measure of Clustering Balance
Emanuele Bossi, C. Tyler Diggans, Abd AlRahman R. AlMomani
The Boltzmann-Shannon Index (BSI) for clustered continuous data is introduced as a normalized measure that captures the relationship between geometry-based and frequency-based prob…
Generalizing Geometric Partition Entropy for the Estimation of Mutual Information in the Presence of Informative Outliers
C. Tyler Diggans, Abd AlRahman R. AlMomani
The recent introduction of geometric partition entropy brought a new viewpoint to non-parametric entropy quantification that incorporated the impacts of informative outliers, but i…
The Essential Synchronization Backbone Problem
C. Tyler Diggans, Jeremie Fish, Abd AlRahman R. AlMomani +1
Network optimization strategies for the process of synchronization have generally focused on the re-wiring or re-weighting of links in order to: (1) expand the range of coupling st…
ERFit: Entropic Regression Fit Matlab Package, for Data-Driven System Identification of Underlying Dynamic Equations
Abd AlRahman AlMomani, Erik Bollt
Data-driven sparse system identification becomes the general framework for a wide range of problems in science and engineering. It is a problem of growing importance in applied mac…
Entropic Causal Inference for Neurological Applications
Jeremie Fish, Alexander DeWitt, Abd AlRahman R. AlMomani +2
The ultimate goal of cognitive neuroscience is to understand the mechanistic neural processes underlying the functional organization of the brain. Key to this study is understandin…
Data-Driven Learning of Boolean Networks and Functions by Optimal Causation Entropy Principle (BoCSE)
Jie Sun, Abd AlRahman AlMomani, Erik Bollt
Boolean functions and networks are commonly used in the modeling and analysis of complex biological systems, and this paradigm is highly relevant in other important areas in data s…
Informative Ranking of Stand Out Collections of Symptoms: A New Data-Driven Approach to Identify the Strong Warning Signs of COVID 19
Abd AlRahman AlMomani, Erik Bollt
We develop here a data-driven approach for disease recognition based on given symptoms, to be efficient tool for anomaly detection. In a clinical setting and when presented with a…
An Early Warning Sign of Critical Transition in The Antarctic Ice Sheet -- A Data Driven Tool for Spatiotemporal Tipping Point
Abd AlRahman AlMomani, Erik Bollt
Our recently developed tool, called Directed Affinity Segmentation was originally designed for data-driven discovery of coherent sets in fluidic systems. Here we interpret that it…
How Entropic Regression Beats the Outliers Problem in Nonlinear System Identification
Abd AlRahman R. AlMomani, Jie Sun, Erik Bollt
In this work, we developed a nonlinear System Identification (SID) method that we called Entropic Regression. Our method adopts an information-theoretic measure for the data-driven…
Go With the Flow, on Jupiter and Snow. Coherence From Model-Free Video Data without Trajectories
Abd AlRahman AlMomani, Erik M. Bollt
Viewing a data set such as the clouds of Jupiter, coherence is readily apparent to human observers, especially the Great Red Spot, but also other great storms and persistent struct…