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D-Index & Metrics

Mathematics

D-Index
56
Citations
15141
World Ranking
726
National Ranking
359

Overview

Brian R. Hunt is affiliated with the University of Maryland, College Park in the United States. The primary focus of their research lies in Environmental Science, with a particular emphasis on Atmospheric Science, Artificial Intelligence, and Global and Planetary Change. Their work also spans subfields such as Ecology and Statistical and Nonlinear Physics.

The scientist's research explores several main topics, including Meteorological Phenomena and Simulations, Neural Networks and Reservoir Computing, Climate Variability and Models, Model Reduction and Neural Networks, Mental Health Research Topics, Environmental DNA in Biodiversity Studies, and Microbial Community Ecology and Physiology.

Recent publications by Brian R. Hunt include the following:

  • A Machine Learning-Based Global Atmospheric Forecast Model, 2020, Geophysical Research Letters
  • A Hybrid Approach to Atmospheric Modeling That Combines Machine Learning With a Physics-Based Numerical Model, 2022, Journal of Advances in Modeling Earth Systems
  • A Hybrid Atmospheric Model Incorporating Machine Learning Can Capture Dynamical Processes Not Captured by Its Physics-Based Component, 2023, Geophysical Research Letters
  • Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics, 2020, Neural Networks
  • Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing, 2023, Neural Networks

The frequent co-authors in their research include Edward Ott, Evgeny A. Pakhomov, Troy Arcomano, Istvan Szunyogh, and Alexander Wikner.

Brian R. Hunt has published primarily in venues such as Technical Reports, arXiv (Cornell University), Geophysical Research Letters, Journal of Advances in Modeling Earth Systems, and Neural Networks.

Best Publications

  • Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter

    Brian R. Hunt;Eric J. Kostelich;Istvan Szunyogh

  • Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach

    Jaideep Pathak;Brian Hunt;Michelle Girvan;Zhixin Lu

  • A local ensemble Kalman filter for atmospheric data assimilation

    Edward Ott;Brian R. Hunt;Istvan Szunyogh;Aleksey V. Zimin

  • Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data

    Jaideep Pathak;Zhixin Lu;Brian R. Hunt;Michelle Girvan

  • Prevalence: a translation-invariant “almost every” on infinite-dimensional spaces

    Brian R. Hunt;Timothy Sauer;James A. Yorke

  • Reducing storage requirements for biological sequence comparison

    Michael Roberts;Wayne Hayes;Brian R. Hunt;Stephen M. Mount

  • A Guide to Matlab: For Beginners and Experienced Users

    Brian R. Hunt;Ronald L. Lipsman;Jonathan M. Rosenberg

  • Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics

    Pantelis R. Vlachas;Jaideep Pathak;Brian R. Hunt;Themistoklis P. Sapsis

  • Onset of synchronization in large networks of coupled oscillators.

    Juan G. Restrepo;Edward Ott;Brian R. Hunt

  • Reservoir observers: Model-free inference of unmeasured variables in chaotic systems

    Zhixin Lu;Jaideep Pathak;Brian Hunt;Michelle Girvan

  • Four-dimensional ensemble Kalman filtering

    B. R. Hunt;E. Kalnay;Eric Kostelich;E. Ott

  • Characterizing the Dynamical Importance of Network Nodes and Links

    Juan G. Restrepo;Edward Ott;Brian R. Hunt

  • Balance and Ensemble Kalman Filter Localization Techniques

    Steven J. Greybush;Eugenia Kalnay;Takemasa Miyoshi;Kayo Ide

  • Differentiable generalized synchronization of chaos

    Brian R. Hunt;Edward Ott;James A. Yorke

  • Local low dimensionality of atmospheric dynamics.

    D. J. Patil;Brian R. Hunt;Eugenia Kalnay;James A. Yorke

  • Map with more than 100 coexisting low-period periodic attractors.

    Ulrike Feudel;Celso Grebogi;Brian R. Hunt;James A. Yorke

  • Approximating the largest eigenvalue of network adjacency matrices.

    Juan G. Restrepo;Edward Ott;Brian R. Hunt

  • Assessing a local ensemble Kalman filter: perfect model experiments with the National Centers for Environmental Prediction global model

    Istvan Szunyogh;Eric J. Kostelich;G. Gyarmati;D. J. Patil

  • A comparative study of 4D-VAR and a 4D Ensemble Kalman Filter: perfect model simulations with Lorenz-96

    Elana J. Fertig;John Harlim;Brian R. Hunt

  • The Hausdorff dimension of graphs of Weierstrass functions

    Brian Hunt

  • The theory of chaotic attractors

    B.R. Hunt;J.A. Kennedy;T.-Y. Li;Helena Nusse

Frequent Co-Authors

Edward Ott
Edward Ott University of Maryland, College Park
James A. Yorke
James A. Yorke University of Maryland, College Park
Eugenia Kalnay
Eugenia Kalnay University of Maryland, College Park
Jonathan Rosenberg
Jonathan Rosenberg University of Maryland, College Park
Kevin R. Coombes
Kevin R. Coombes The Ohio State University
Celso Grebogi
Celso Grebogi University of Aberdeen
Timothy Sauer
Timothy Sauer George Mason University
Petros Koumoutsakos
Petros Koumoutsakos Harvard University
Daniel P. Lathrop
Daniel P. Lathrop University of Maryland, College Park
James A. Carton
James A. Carton University of Maryland, College Park

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