Chaotic, Topology, Statistical physics, Synchronization networks and Dynamical systems theory are his primary areas of study. His research in Chaotic intersects with topics in Configuration space, Attractor, Symbolic dynamics and Three-body problem. His Topology research is multidisciplinary, incorporating perspectives in Telecommunications network, Synchronization and Nonlinear system.
His research integrates issues of Nonlinear time series analysis, Contrast, Transfer entropy and Dimension in his study of Statistical physics. His Synchronization networks study combines topics in areas such as Distributed computing, Parametric statistics, Stability, Master stability function and Eigenvalues and eigenvectors. By researching both Dynamical systems theory and Synchronization, he produces research that crosses academic boundaries.
Erik M. Bollt spends much of his time researching Dynamical systems theory, Topology, Chaotic, Algorithm and Theoretical computer science. As a part of the same scientific family, Erik M. Bollt mostly works in the field of Dynamical systems theory, focusing on Applied mathematics and, on occasion, Eigenfunction and Discrete mathematics. In Topology, Erik M. Bollt works on issues like Synchronization, which are connected to Network topology.
He has included themes like Attractor, Mathematical analysis, Control theory, Nonlinear system and Symbolic dynamics in his Chaotic study. His Algorithm research includes elements of Probability and statistics, Measure and Dimensionality reduction. While the research belongs to areas of Theoretical computer science, he spends his time largely on the problem of Entropy, intersecting his research to questions surrounding Time series, Inference and Mutual information.
Erik M. Bollt mainly investigates Algorithm, Dynamical systems theory, Entropy, Applied mathematics and Data-driven. He interconnects Nonlinear dimensionality reduction, Probability and statistics, Embedding, Robustness and Complex network in the investigation of issues within Algorithm. In his research on the topic of Dynamical systems theory, Reservoir computing is strongly related with Dynamical system.
In the field of Entropy, his study on Entropy overlaps with subjects such as Causation. Erik M. Bollt focuses mostly in the field of Applied mathematics, narrowing it down to topics relating to Eigenfunction and, in certain cases, Equivalence class, Rank and Nonlinear system. His work focuses on many connections between Inference and other disciplines, such as Feature selection, that overlap with his field of interest in Theoretical computer science.
His scientific interests lie mostly in Algorithm, Causation, Dynamical systems theory, Entropy and Inference. His studies deal with areas such as Nonlinear dimensionality reduction, Dimensionality reduction, Manifold, Probability and statistics and Robustness as well as Algorithm. Data science, Earth system science and Nonlinear system are fields of study that overlap with his Causation research.
While working in this field, Erik M. Bollt studies both Dynamical systems theory and Rectification. His Entropy research incorporates themes from Estimation theory, Mutual information and Time series. Erik M. Bollt has researched Inference in several fields, including Information flow and Anatomy.
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Local method for detecting communities.
James P. Bagrow;Erik M. Bollt.
Physical Review E (2005)
Sufficient Conditions for Fast Switching Synchronization in Time-Varying Network Topologies
Daniel J. Stilwell;Erik M. Bollt;D. Gray Roberson.
Siam Journal on Applied Dynamical Systems (2006)
Inferring causation from time series in Earth system sciences
Jakob Runge;Jakob Runge;Sebastian Bathiany;Erik Bollt;Gustau Camps-Valls.
Nature Communications (2019)
Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator
Qianxiao Li;Felix Dietrich;Erik M. Bollt;Ioannis G. Kevrekidis.
Master stability functions for coupled nearly identical dynamical systems
Jie Sun;Erik M. Bollt;Takashi Nishikawa.
Causation entropy identifies indirect influences, dominance of neighbors and anticipatory couplings
Jie Sun;Erik M. Bollt.
Physica D: Nonlinear Phenomena (2014)
Hybrid chaos synchronization and its application in information processing
Qingxian Xie;Guanrong Chen;E. M. Bollt.
Mathematical and Computer Modelling (2002)
What is special about diffusion on scale-free nets?
Erik M Bollt;Daniel ben-Avraham.
New Journal of Physics (2005)
Causal Network Inference by Optimal Causation Entropy
Jie Sun;Dane Taylor;Erik M. Bollt.
Siam Journal on Applied Dynamical Systems (2015)
Random talk: Random walk and synchronizability in a moving neighborhood network☆
Maurizio Porfiri;Daniel J. Stilwell;Erik M. Bollt;Joseph D. Skufca.
Physica D: Nonlinear Phenomena (2006)
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