Publications

William T. Redman, Dean Huang, Maria Fonoberova, Igor Mezic, Koopman Learning with Episodic Memory, 2023, arXiv preprint arXiv:2311.12615v1

Allan M. Avila and Igor Mezic, Spectral Properties of Pullback Operators on Vector Bundles of a Dynamical System, SIAM Journal on Applied Dynamical Systems, 2023, 22(4), 3059-3092. https://doi.org/10.1137/22M1492064

Igor Mezic, Zlatko Drmac, Nelida Crnjaric-Zic, Senka Macesic, Maria Fonoberova, Ryan Mohr, Allan Avila, Iva Manojlovic, Aleksandr Andrejcuk, A Koopman Operator-Based Prediction Algorithm and its Application to COVID-19 Pandemic, 2023, arXiv preprint arXiv:2304.13601

Alan B. Cao, Michael Planer, James Hogg, Maria Fonoberova, Lake Bookman, Tyler Macdonald, Jennifer Hemingway, Qinghua Ding, and Igor Mezic, Tipping-Points and Robustness of Sea Ice using Koopman Mode Decomposition, AAAI 2023 Spring Symposium Series: AI for Climate Tipping-Point Discovery.

Z. Drmač, A LAPACK implementation of the Dynamic Mode Decomposition I, LAPACK Working Note 298.

Z. Drmač, A LAPACK implementation of the Dynamic Mode Decomposition II, LAPACK Working Note 300.

R. Mohr, M. Fonoberova, and I. Mezic, Koopman Reduced Order Modeling with Confidence Bounds, 2022, arXiv preprint arXiv:2209.13127

W.T. Redman, M. Fonoberova, R. Mohr, and I.G. Kevrekidis, Discovering Sparse Subnetworks via Koopman Mode Decomposition, IEICE Proceedings Series, 71(B1L-B-03), 2022.

W.T. Redman, M. Fonoberova, R. Mohr, I.G. Kevrekidis, and I. Mezic, An Operator Theoretic View on Pruning Deep Neural Networks, International Conference on Learning Representations (ICLR), 2022. https://openreview.net/forum?id=pWBNOgdeURp

W.T. Redman, M. Fonoberova, R. Mohr, I.G. Kevrekidis, and I. Mezic, Algorithmic (Semi-)Conjugacy via Koopman Operator Theory, 2022 IEEE 61st Conference on Decision and Control (CDC). https://ieeexplore.ieee.org/abstract/document/9992592

A.M. Avila, M. Fonoberova, J.P. Hespanha, I. Mezic, D. Clymer, J. Goldstein, M.A. Pravia, and D. Javorsek II, Game Balancing using Koopman-based Learning, Proceedings of 2021 American Control Conference (ACC), May 26-28, 2021, New Orleans, USA. https://dx.doi.org/10.23919/ACC50511.2021.9483027

R. Mohr, M. Fonoberova, I. Manojlovic, A. Andrejcuk, Z. Drmac, Y. Kevrekidis, and I. Mezic, Applications of Koopman Mode Analysis to Neural Networks, Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22-24, 2021. https://arxiv.org/pdf/2006.11765.pdf

R. Mohr, A. Avila, S. Gosh, A. Bhattarai, M. Yang, X. Feng, M. Head-Gordon, R. Salakhutdinov, M. Fonoberova, and I. Mezic, Combining Programmable Potentials and Neural Networks for Materials Problems, Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22-24, 2021.

R. Mohr, M. Fonoberova, Z. Drmac, I. Manojlovic, and I. Mezic, Predicting the Critical Number of Layers for Hierarchical Support Vector Regression, Entropy 2021, 23, 37. https://doi.org/10.3390/e23010037

Z. Drmač, I. Mezić, and R. Mohr, Identification of nonlinear systems using the infinitesimal generator of the Koopman memigroup – a numerical implementation of the Mauroy-Goncalves method, Mathematics 9 (17), 2075, 2021.

R. Mohr, and I. Mezic, Koopman spectrum and stability of cascaded dynamical systems, The Koopman Operator in Systems and Control: Concepts, Methodologies, and Applications, 99-129, 2020.

Z. Drmač, I. Mezić, and R. Mohr, On least squares problem with certain Vandermonde-Khatri-Rao structure with applications to DMD, SIAM Journal on Scientific Computing, 42(5), A3250–A3284., 2020.

Z. Drmač, Dynamic Mode Decomposition – a numerical linear algebra perspective, The Koopman Operator in Systems and Control: Concepts, Methodologies, and Applications, Vol. 484, Springer 2020.

J. Hogg, M. Fonoberova, and I. Mezic, Exponentially decaying modes and long-term prediction of sea ice concentration using Koopman mode decomposition, Sci Rep 10, 16313, 2020. https://doi.org/10.1038/s41598-020-73211-z

C. Folkestad, D. Pastor, I. Mezic, R. Mohr, M. Fonoberova, and J. Burdick, Extended Dynamic Mode Decomposition with Learned Koopman Eigenfunctions for Prediction and Control, 2020 American Control Conference (ACC), Denver, CO, USA, 2020, pp. 3906-3913. doi: 10.23919/ACC45564.2020.9147729

M. Fonoberova, I. Mezic, J,. Mezic, J. Hogg, and J. Gravel, Small-world networks and synchronisation in an agent-based model of civil violence, Global Crime, 20:3-4, 161-195, 2019. https://doi.org/10.1080/17440572.2019.1662304

J. Hogg, M. Fonoberova, I. Mezic, and R. Mohr, Koopman Mode Analysis of agent-based models of logistics processes, PLoS ONE 14(9): e0222023, 2019. https://doi.org/10.1371/journal.pone.0222023

I. Mezic, V. A. Fonoberov, M. Fonoberova, and T. Sahai, Spectral Complexity of Directed Graphs and Application to Structural Decomposition, Complexity, vol. 2019, Article ID 9610826, 18 pages, 2019. https://doi.org/10.1155/2019/9610826

Z. Drmač, I. Mezić, and R. Mohr, Data-driven Koopman spectral analysis in Vandermonde-Cauchy form via the DFT: numerical method and theoretical insights, SIAM Journal on Scientific Computing 41 (5), A3118-A3151. 2019.

Z. Drmač, I. Mezić, and R. Mohr, Data driven modal decompositions: analysis and enhancements, SIAM Journal on Scientific Computing, 40 (4) (2018), A2253–A2285. 2018.

M. Fonoberova, I. Mezic, J. Mezic, and R. Mohr, An agent-based model of urban insurgence: Effect of gathering sites and Koopman mode analysis, PLOS ONE 13(10): e0205259, 2018. https://doi.org/10.1371/journal.pone.0205259

F. Buckman, E. G. Vaschillo, M. Fonoberova, I. Mezic, and M. E. Bates, The Translational Value of Psychophysiology Methods and Mechanisms: Multilevel, Dynamic, Personalized, Journal of Studies on Alcohol and Drugs, 79(2), 229–238, 2018.

B. Glaz, I. Mezic, M. Fonoberova, and S. Loire, Quasi-periodic intermittency in oscillating cylinder flow, J. Fluid Mech., vol. 828, pp. 680–707, 2017. doi:10.1017/jfm.2017.530