NEWS    Saviz Mowlavi gave an invited talk at North Carolina State University

Date:

April 12, 2024

Description:

Saviz Mowlavi was invited to present remotely at the Computational and Applied Mathematics seminar series in the Department of Mathematics at North Carolina State University.

The talk, entitled "Model-based and data-driven prediction and control of spatio-temporal systems", described the use of temporal smoothness to regularize the training of fast surrogate models for PDEs, user-friendly methods for PDE-constrained optimization, and efficient strategies for learning feedback controllers for PDEs.

External Link:

https://math.sciences.ncsu.edu/event/computational-and-applied-mathematics-seminar-saviz-mowlavi-merl-model-based-and-data-driven-prediction-and-control-of-spatio-temporal-systems/

Research Areas:

Control, Dynamical Systems, Machine Learning, Optimization

• Related Publications

   Mowlavi, S., Nabi, S., "Optimal control of PDEs using physics-informed neural networks", Journal of Computational Physics, DOI: j.jcp.2022.111731, Vol. 473, pp. 111731, October 2022.

  BibTeX TR2022-143 PDF

  • @article{Mowlavi2022oct,
  • author = {Mowlavi, Saviz and Nabi, Saleh},
  • title = {{Optimal control of PDEs using physics-informed neural networks}},
  • journal = {Journal of Computational Physics},
  • year = 2022,
  • volume = 473,
  • pages = 111731,
  • month = oct,
  • doi = {j.jcp.2022.111731},
  • url = {https://www.merl.com/publications/TR2022-143}
  • }