Mar 15, 2022 · In this paper, we discuss techniques for effective incorporation of important physical phenomena in the training process.

Nov 13, 2021 · A review of deep learning approaches for inverse scattering problems. ... Physics-inspired convolutional neural network for solving full ...

Jan 19, 2024 · Deep neural network (DNN) techniques have been applied to solve nonlinear electromagnetic inverse scattering problems (ISP) and shown potentials ...

Apr 1, 2022 · Effects of physics-guided loss functions are studied using a variety of numerical experiments. Pros and cons of the investigated ISP solvers ...

New designs of loss functions are proposed which incorporate multiple-scattering based near-field quantities (such as scattered fields or induced currents ...

Physics-guided Loss Functions Improve Deep Learning Performance in Inverse Scattering ... Preprints and early-stage research may not have been peer reviewed yet.

In this paper, we propose a data-driven framework for inverse scattering based on deep generative models. Our approach learns a low-dimensional manifold as a ...

Physics-guided Loss Functions Improve Deep Learning Performance in Inverse Scattering · no code implementations • 13 Nov 2021 • Zicheng Liu, Mayank Roy, Dilip ...

Mar 22, 2024 · Our recent paper titled “Physics-Guided Loss Functions Improve Deep Learning Performance in Inverse Scattering” has been recognized for its ...

Physics-Guided Loss Functions Improve Deep Learning Performance in Inverse Scattering · Physics. IEEE Transactions on Computational Imaging · 2022.