Ph.D. Candidate, Applied Mathematics and Statistics

University of California Santa Cruz

I work at the exciting interface between machine learning and dynamical systems.
Specifically, I seek novel ways to apply machine learning tools for modeling and computational optimal control of high-dimensional nonlinear systems under uncertainty.
In addition, my research also explores how domain knowledge from physics and control theory can be used to improve such machine learning methods.
More generally, I am interested in computational optimal control, dynamical systems, machine learning, and aerospace applications.

I received my B.A. from UC Santa Cruz with a double major in pure mathematics and language studies in 2015.
Since 2016, I have been working towards a Ph.D. in applied mathematics and statistics, also at UC Santa Cruz.
In 2020 I started a Pathways Internshep at NASA, first at Ames Research Center in the Aviation Systems division and now at Langley Research Center in the Flight Dynamics branch.

Email: tenakamu AT ucsc DOT edu

LinkedIn

T. Nakamura-Zimmerer, Q. Gong, and W. Kang. *Neural network optimal feedback control with guaranteed local stability.*
arXiv: 2205.00394 [math.OC], 2022.

T. Nakamura-Zimmerer, Q. Gong, and W. Kang. *Neural network optimal feedback control with enhanced closed loop stability.*
arXiv: 2109.07466 [math.OC], 2021.

T. Nakamura-Zimmerer, Q. Gong, and W. Kang. *QRnet: optimal regulator design with LQR-augmented neural networks.*
IEEE Control Systems Letters, 2021.

W. Kang, Q. Gong, and T. Nakamura-Zimmerer. *Algorithms of data development for deep learning and feedback design.*
Physica D: Nonlinear Phenomena, 2021.

T. Nakamura-Zimmerer, Q. Gong, and W. Kang. *Adaptive deep learning for high-dimensional Hamilton-Jacobi-Bellman equations.*
SIAM Journal on Scientific Computing, 2021.

T. Nakamura-Zimmerer, Q. Gong, and W. Kang. *A Causality-free neural network method for high-dimensional Hamilton-Jacobi-Bellman equations.*
American Control Conference, 2020.

T. Nakamura-Zimmerer, D. Venturi, Q. Gong, and W. Kang. *Density propagation with characteristics-based deep learning.*
arXiv: 1911.09311 [math.DS], 2019.

*Improving reliability in neural network optimal feedback control design.*
SIAM Conference on Control and its Applications, Spokane WA, 2021.

*QRnet: optimal regulator design with LQR-augmented neural networks.*
American Control Conference, New Orleans LA, 2021.

*Supervised learning for optimal feedback controller design.*
NorCal Control Workshop, Stanford CA, 2021.

*Data-driven approximation of high-dimensional flow maps and probability distributions using deep neural networks.*
SIAM Conference on Computational Science and Engineering, Spokane WA, 2019.

*Deep learning for probability density and flow map approximation of high dimensional nonlinear systems.*
Bay Area Scientific Computing Day, Sandia National Laboratories, Livermore CA, 2018.

*Data-driven computational optimal control for nonlinear systems under uncertainty.*
SIAM Annual Meeting, Portland OR, 2018.

Copyright Tenavi Nakamura-Zimmerer, 2022. Website under construction.