Igor Mezic
· FacultyUniversity of California, Santa Barbara · Mathematics
Active 1994–2025
Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.
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 authorCorrespondingThe 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
articleSoft 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…
Scientific Reports · 2024-03-09 · 11 citations
articleOpen access1st authorCorrespondingFuture 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
Project IMPACT: In-the-Moment Protection from Automatic Capture by Triggers
NIH · $2.7M · 2015–2020
Design of attractors for enhanced sensitivity biosensing
NSF · $310k · 2005–2008
Frequent coauthors
- 49 shared
Yoshihiko Susuki
Kyoto University
- 48 shared
Ryan Mohr
- 46 shared
Maria Fonoberova
- 33 shared
Sophie Loire
Bruker (United States)
- 32 shared
Marko Budišić
- 30 shared
Milan Korda
Laboratoire d'Analyse et d'Architecture des Systèmes
- 26 shared
Thai Son Doan
- 26 shared
Stefan Siegmund
Education
- 1994
Ph. D., Applied Mechanics
California Institute of Technology
- 1990
Dipl. Ing., Mechanical Engineering
University of Rijeka
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