World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
41
Citations
7606
World Ranking
1898
National Ranking
808

Engineering and Technology

D-Index
42
Citations
8705
World Ranking
6441
National Ranking
1768

Overview

Erik M. Bollt is affiliated with Clarkson University in the United States and has an extensive research portfolio primarily spanning Computer Science and Physics and Astronomy. Their scholarly work includes a significant focus on domains such as Statistical and Nonlinear Physics, Artificial Intelligence, Molecular Biology, Cognitive Neuroscience, and Control and Systems Engineering.

The research topics Erik M. Bollt has contributed to cover diverse areas in computational and physical sciences. Key topics include Model Reduction and Neural Networks, Neural Networks and Applications, Complex Network Analysis Techniques, Nonlinear Dynamics and Pattern Formation, Neural Dynamics and Brain Function, Neural Networks and Reservoir Computing, and Gene Regulatory Network Analysis.

Among their recent publications are:

  • On explaining the surprising success of reservoir computing forecaster of chaos? The universal machine learning dynamical system with contrast to VAR and DMD (2021, Chaos An Interdisciplinary Journal of Nonlinear Science)
  • How entropic regression beats the outliers problem in nonlinear system identification (2020, Chaos An Interdisciplinary Journal of Nonlinear Science)
  • Intralayer Synchronization in Evolving Multiplex Hypernetworks: Analytical Approach (2020, SIAM Journal on Applied Dynamical Systems)
  • Decoding collective communications using information theory tools (2020, Journal of The Royal Society Interface)
  • Predicting sea surface temperatures with coupled reservoir computers (2022, Nonlinear Processes in Geophysics)

The venues where Erik M. Bollt most frequently publishes reflect their interdisciplinary focus, including:

  • arXiv (Cornell University)
  • Chaos An Interdisciplinary Journal of Nonlinear Science
  • Scientific Reports
  • SIAM Journal on Applied Dynamical Systems
  • MDPI (MDPI AG)

Collaboration is a notable component of their research activity, with frequent coauthors including Jeremie Fish, Abd AlRahman R. AlMomani, Paul J. Laurienti, Jie Sun, and Sudam Surasinghe. This indicates active participation in collaborative research efforts across various topics and disciplines.

Best Publications

  • Inferring causation from time series in Earth system sciences

    Jakob Runge;Jakob Runge;Sebastian Bathiany;Erik Bollt;Gustau Camps-Valls

  • Next generation reservoir computing.

    Daniel J. Gauthier;Erik Bollt;Aaron Griffith;Wendson A. S. Barbosa

  • 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

  • Sufficient Conditions for Fast Switching Synchronization in Time-Varying Network Topologies

    Daniel J. Stilwell;Erik M. Bollt;D. Gray Roberson

  • Causal Network Inference by Optimal Causation Entropy

    Jie Sun;Dane Taylor;Erik M. Bollt

  • 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

  • Hybrid chaos synchronization and its application in information processing

    Qingxian Xie;Guanrong Chen;E. M. Bollt

  • An information-theoretic, all-scales approach to comparing networks

    James P. Bagrow;Erik M. Bollt

  • What is special about diffusion on scale-free nets?

    Erik M Bollt;Daniel ben-Avraham

  • On Explaining the Surprising Success of Reservoir Computing Forecaster of Chaos? The Universal Machine Learning Dynamical System with Contrasts to VAR and DMD

    Erik Bollt

  • Synchronization in Random Weighted Directed Networks

    M. Porfiri;D.J. Stilwell;E.M. Bollt

  • Random talk: Random walk and synchronizability in a moving neighborhood network☆

    Maurizio Porfiri;Daniel J. Stilwell;Erik M. Bollt;Joseph D. Skufca

  • Communication and synchronization in, disconnected networks with dynamic topology: moving neighborhood networks.

    Joseph D. Skufca;Erik M. Bollt;Erik M. Bollt

  • Targeting chaotic orbits to the Moon through recurrence

    Erik M Bollt;James D Meiss

  • CODING, CHANNEL CAPACITY, AND NOISE RESISTANCE IN COMMUNICATING WITH CHAOS

    Erik Bollt;Ying Cheng Lai;Celso Grebogi

  • Master Stability Functions for Coupled Near-Identical Dynamical Systems

    Jie Sun;Erik M. Bollt;Takashi Nishikawa

  • Applied and Computational Measurable Dynamics

    Erik M. Bollt;Naratip Santitissadeekorn

  • Estimating generating partitions of chaotic systems by unstable periodic orbits

    Ruslan L. Davidchack;Ying-Cheng Lai;Erik M. Bollt;Mukeshwar Dhamala;Mukeshwar Dhamala;Mukeshwar Dhamala

  • High resolution MEMS accelerometers to estimate VO2 and compare running mechanics between highly trained inter-collegiate and untrained runners.

    Stephen J. McGregor;Michael A. Busa;James A. Yaggie;Erik M. Bollt

  • Stochastic synchronization over a moving neighborhood network

    M. Porfiri;D.J. Stilwell;E.M. Bollt;J.D. Skufca

Frequent Co-Authors

Maurizio Porfiri
Maurizio Porfiri New York University
Daniel ben-Avraham
Daniel ben-Avraham Clarkson University
Ioannis G. Kevrekidis
Ioannis G. Kevrekidis Johns Hopkins University
Ying-Cheng Lai
Ying-Cheng Lai Arizona State University
Pier Marzocca
Pier Marzocca RMIT University
Mason A. Porter
Mason A. Porter University of California, Los Angeles
James D. Meiss
James D. Meiss University of Colorado Boulder
Nicholas T. Ouellette
Nicholas T. Ouellette Stanford University
Goodarz Ahmadi
Goodarz Ahmadi Clarkson University
Jakob Zscheischler
Jakob Zscheischler Helmholtz Centre for Environmental Research

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