# Regularization of Nonlinear Inverse Problems -- From Functional Analysis to Data-Driven Approaches

arXiv: https://arxiv.org/abs/2506.17465 · HTML: https://mlsanity.com/paper/2506.17465/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2025.

## Built on by (most cited first, 1 of 1)

- [Data-Consistent Learning of Inverse Problems](https://mlsanity.com/paper/2601.12831/) (2026)

## Abstract (arXiv)

The focus of this book is on the analysis of regularization methods for solving nonlinear inverse problems. Specifically, we place a strong emphasis on techniques that incorporate supervised or unsupervised data derived from prior experiments. This approach enables the integration of data-driven insights into the solution of inverse problems governed by physical models. Inverse problems, in general, aim to uncover the inner mechanisms of an observed system based on indirect or incomplete measurements. This field has far-reaching applications across various disciplines, such as medical or geophysical imaging, as well as, more broadly speaking, industrial processes where identifying hidden parameters is essential.