A differentiable physics-informed gradient descent framework for CTR phase retrieval matches traditional methods in fidelity on synthetic data while enabling seamless inclusion of complex experimental effects and multi-diagnostic constraints.
Deep learning based on parameterized physical forward model for adaptive holographic imaging with unpaired data
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Adaptable phase retrieval for coherent transition radiation spectroscopy based on differentiable physics information
A differentiable physics-informed gradient descent framework for CTR phase retrieval matches traditional methods in fidelity on synthetic data while enabling seamless inclusion of complex experimental effects and multi-diagnostic constraints.