Oct 15, 2023 · We introduce a novel approach, denoted as the "data-regularized operator learning" (DaROL) method, designed to address PDE inverse problems. The ...

Apr 5, 2024 · We introduce a novel approach, denoted as the "data-regularized operator learning" (DaROL) method, designed to address PDE inverse problems. The ...

Regularization plays a critical role in incorporating prior information into inverse problems. While numerous deep learning methods have been proposed to tackle ...

Innovative "DaROL" method combines deep learning with regularization to address inverse problems effectively.

Oct 15, 2023 · Regularization plays a pivotal role in integrating prior information into inverse problems. While many deep learning methods have been ...

Apr 27, 2024 · Let Data Talk: Data-regularized Operator Learning Theory for Inverse Problems ... Neural Inverse Operators for Solving PDE Inverse Problems.

It is shown that neural networks can be used for the data-driven solution of inverse problems and existing deep learning methods for inverse problems are ...

Deep Learning Generalization. [8] K. Chen^, C. Wang^, H. Yang^*. Let Data Talk: Data-Regularized Operator Learning Theory for Inverse Problems. [pdf]. [7] H ...

Let data talk: data-regularized operator learning theory for inverse problems. (with Chunmei Wang and Haizhao Yang, arXiv 2310.09854). Publications. Deep ...

Missing: let operator