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Found 3 papers, 2 papers with code
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions
To this end, we propose a physics-infused deep neural network based on the Blaschke products for phase retrieval.
Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials
Recently, it has been shown that deep learning can be an alternative solution to infer the relationship between an AEM geometry and its properties using a (relatively) small pool of CEMS data.
Benchmarking deep inverse models over time, and the neural-adjoint method
We consider the task of solving generic inverse problems, where one wishes to determine the hidden parameters of a natural system that will give rise to a particular set of measurements.
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