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Igor Mezic

· Faculty

University of California, Santa Barbara · Mathematics

Active 1994–2025

h-index60
Citations18.9k
Papers47472 last 5y
Funding$3.0M

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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Research topics

  • Computer Science
  • Data Mining
  • Engineering
  • Pure mathematics
  • Transport engineering
  • Algorithm
  • Ecology
  • Operations research
  • Mathematics

Selected publications

  • The Koopman Operator in Systems and Control

    Lecture notes in control and information sciences · 2020 · 343 citations

  • Data-driven analysis and forecasting of highway traffic dynamics

    Nature Communications · 2020 · 154 citations

    Senior authorCorresponding

    The unpredictable elements involved in a vehicular traffic system, like human interaction and weather, lead to a very complicated, high-dimensional, nonlinear dynamical system. Therefore, it is difficult to develop a mathematical or artificial intelligence model that describes the time evolution of traffic systems. All the while, the ever-increasing demands on transportation systems has left traffic agencies in dire need of a robust method for analyzing and forecasting traffic. Here we demonstra…

  • Control of soft robots with inertial dynamics

    Science Robotics · 2023-08-30 · 117 citations

    article

    Soft robots promise improved safety and capability over rigid robots when deployed near humans or in complex, delicate, and dynamic environments. However, infinite degrees of freedom and the potential for highly nonlinear dynamics severely complicate their modeling and control. Analytical and machine learning methodologies have been applied to model soft robots but with constraints: quasi-static motions, quasi-linear deflections, or both. Here, we advance the modeling and control of soft robots…

  • A Koopman operator-based prediction algorithm and its application to COVID-19 pandemic and influenza cases

    Scientific Reports · 2024-03-09 · 11 citations

    articleOpen access1st authorCorresponding

    Future state prediction for nonlinear dynamical systems is a challenging task. Classical prediction theory is based on a, typically long, sequence of prior observations and is rooted in assumptions on statistical stationarity of the underlying stochastic process. These algorithms have trouble predicting chaotic dynamics, "Black Swans" (events which have never previously been seen in the observed data), or systems where the underlying driving process fundamentally changes. In this paper we develo…

  • On Higher Order Drift and Diffusion Estimates for Stochastic SINDy

    SIAM Journal on Applied Dynamical Systems · 2024-06-14 · 8 citations

    articleSenior author

Recent grants

Frequent coauthors

  • Yoshihiko Susuki

    Kyoto University

    49 shared
  • Ryan Mohr

    48 shared
  • Maria Fonoberova

    46 shared
  • Sophie Loire

    Bruker (United States)

    33 shared
  • Marko Budišić

    32 shared
  • Milan Korda

    Laboratoire d'Analyse et d'Architecture des Systèmes

    30 shared
  • Thai Son Doan

    26 shared
  • Stefan Siegmund

    26 shared

Education

  • Ph. D., Applied Mechanics

    California Institute of Technology

    1994
  • Dipl. Ing., Mechanical Engineering

    University of Rijeka

    1990

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