| Ergodic theory, dynamic mode decomposition, and computation of spectral properties of the Koopman operator H Arbabi, I Mezic SIAM Journal on Applied Dynamical Systems 16 (4), 2096-2126, 2017 | 562 | 2017 |
| A data-driven koopman model predictive control framework for nonlinear partial differential equations H Arbabi, M Korda, I Mezić 2018 IEEE Conference on Decision and Control (CDC), 6409-6414, 2018 | 136 | 2018 |
| Study of dynamics in post-transient flows using Koopman mode decomposition H Arbabi, I Mezić Physical Review Fluids 2 (12), 124402, 2017 | 118* | 2017 |
| A data-driven Koopman model predictive control framework for nonlinear flows H Arbabi, M Korda, I Mezic arXiv preprint arXiv:1804.05291, 2018 | 57 | 2018 |
| Linking machine learning with multiscale numerics: Data-driven discovery of homogenized equations H Arbabi, JE Bunder, G Samaey, AJ Roberts, IG Kevrekidis Jom 72, 4444-4457, 2020 | 49 | 2020 |
| Generative stochastic modeling of strongly nonlinear flows with non-Gaussian statistics H Arbabi, T Sapsis SIAM/ASA Journal on Uncertainty Quantification 10 (2), 555-583, 2022 | 23* | 2022 |
| Introduction to Koopman operator theory of dynamical systems H Arbabi Introduction to Koopman operator theory of dynamical systems, 2018 | 23 | 2018 |
| Search strategy in a complex and dynamic environment: The MH370 case S Ivić, B Crnković, H Arbabi, S Loire, P Clary, I Mezić Scientific Reports 10 (1), 19640, 2020 | 18 | 2020 |
| An operator-theoretic viewpoint to non-smooth dynamical systems: Koopman analysis of a hybrid pendulum N Govindarajan, H Arbabi, L Van Blargian, T Matchen, E Tegling 2016 IEEE 55th Conference on Decision and Control (CDC), 6477-6484, 2016 | 13 | 2016 |
| Particles to partial differential equations parsimoniously H Arbabi, IG Kevrekidis Chaos: An Interdisciplinary Journal of Nonlinear Science 31 (3), 2021 | 10 | 2021 |
| Koopman spectral analysis and study of mixing in incompressible flows H Arbabi University of California, Santa Barbara, 2017 | 10 | 2017 |
| On the Computation of Isostables, Isochrons and Other Spectral Objects of the Koopman Operator Using the Dynamic Mode Decomposition I Mezic, H Arbabi International Symposium on Nonlinear Theory and Its Applications, 2017 | 9* | 2017 |
| Computation of transient koopman spectrum using hankel-dynamic mode decompoisition H Arbabi, I Mezic APS Division of Fluid Dynamics Meeting Abstracts, G1. 009, 2017 | 8 | 2017 |
| Mean subtraction and mode selection in dynamic mode decomposition GS Seenivasaharagavan, M Korda, H Arbabi, I Mezić arXiv preprint arXiv:2105.03607, 2021 | 6 | 2021 |
| Prandtl–Batchelor theorem for flows with quasiperiodic time dependence H Arbabi, I Mezić Journal of Fluid Mechanics 862, R1, 2019 | 6 | 2019 |
| Spectral analysis of mixing in 2D high-Reynolds flows H Arbabi, I Mezic arXiv preprint arXiv:1903.10044, 2019 | 2 | 2019 |
| Coarse-grained and emergent distributed parameter systems from data H Arbabi, FP Kemeth, T Bertalan, I Kevrekidis 2021 American Control Conference (ACC), 4063-4068, 2021 | 1 | 2021 |
| Invariant Consistent Dynamic Mode Decomposition GS Seenivasaharagavan, M Korda, H Arbabi, I Mezić arXiv preprint arXiv:2312.08278, 2023 | | 2023 |
| Learning Coarse-Grained Partial Differential Equations from Fine-Scale Data Via Machine Learning S Lee, G Psarellis, H Arbabi, C Siettos, F Dietrich, G Samaey, ... 2020 Virtual AIChE Annual Meeting, 2020 | | 2020 |
| Dynamical-Systems-Guided Learning of PDEs from Data H Arbabi, T Bertalan, A Roberts, G Samaey, IG Kevrekidis 2020 Virtual AIChE Annual Meeting, 2020 | | 2020 |
Igor MezicUniversity of California, Santa BarbaraVerified email at ucsb.edu