Partially-measured physical system characterization with neural networks
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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.