Partially-measured physical system characterization with neural networks

Babak Rahmani; Christophe Moser; Demetri Psaltis; Eirini Kakkava; Navid Borhani

doi:10.1117/12.2593710

1 August 2021 Partially-measured physical system characterization with neural networks

Babak Rahmani, Christophe Moser, Demetri Psaltis, Eirini Kakkava, Navid Borhani

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 118041M (2021) https://doi.org/10.1117/12.2593710
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

We propose a general neural-network based learning framework to solve highly ill-posed problems to predict a system’s forward and backward response function. Such an approach has applications in target-oriented system’s control in fields such as, optics, neuroscience and robotics. The proposed method is able to find the appropriate continuous space input of a system that results in a desired output, despite the input-output relation being nonlinear, the system being time-variant and\or with incomplete measurements of the systems variables and lack of labeled data required for supervise learning.

Conference Presentation

Citation Download Citation

Babak Rahmani, Christophe Moser, Demetri Psaltis, Eirini Kakkava, and Navid Borhani "Partially-measured physical system characterization with neural networks", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 118041M (1 August 2021); https://doi.org/10.1117/12.2593710

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