Research

Publications

• Sara M. Ichinaga, Francesco Andreuzzi, Nicola Demo, Marco Tezzele, Karl Lapo, Gianluigi Rozza, Steven L. Brunton, and J. Nathan Kutz. “PyDMD: A Python package for robust dynamic mode decomposition.” JMLR. 2024. 25(417):1-9. Available: http://jmlr.org/papers/v25/24-0739.html.

• Karl Lapo, Sara M. Ichinaga, and J. Nathan Kutz. “A method for unsupervised learning of coherent spatiotemporal patterns in multi-scale data.” PNAS. 2024. 122(7):e2415786122. Available: https://www.pnas.org/doi/10.1073/pnas.2415786122.

• Seth M. Hirsh, Sara M. Ichinaga, Steven L. Brunton, J. Nathan Kutz, and Bingni W. Brunton. “Structured time-delay models for dynamical systems with connections to frenet-serret frame.” Proceedings of the Royal Society A. 2021. 477(2254): 20210097. Available: https://doi.org/10.1098/rspa.2021.0097.

Preprints

• Alice C. Schwarze, Sara M. Ichinaga, and Bingni W. Brunton. “Network inference via process motifs for lagged correlation in linear stochastic processes.” Preprint: https://arxiv.org/abs/2208.08871. (2022)

Conferences

• Sara M. Ichinaga, Karl Lapo, J. Nathan Kutz, Steven L. Brunton, and Aleksandr Y. Aravkin. “Dynamic Mode Decomposition Variants and Extensions for Robust Data-Driven Modeling.” Minisymposium talk presented at:

  • SIAM Conference on Computational Science and Engineering 2025 (CSE25)
  • SIAM Conference on Applications of Dynamical Systems (DS25)

• Sara M. Ichinaga, Francesco Andreuzzi, Nicola Demo, Marco Tezzele, Karl Lapo, Gianluigi Rozza, Steven L. Brunton, and J. Nathan Kutz. “Extensions and Open-Source Algorithms for Data-Driven Modeling with Dynamic Mode Decomposition.” Minisymposium talk presented at:

  • SIAM Conference on Uncertainty Quantification 2024 (UQ24)
  • SIAM Conference on Computational Science and Engineering 2023 (CSE23)