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Machine Learning Solutions for Inverse Problems

Name: Machine Learning Solutions for Inverse Problems
Price: 661.75 AUD
Availability: PreOrder

Part B

By: Michael Hintermuller (Editor)

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Hardcover | 1 October 2026

At a Glance

Format
Hardcover

Dimensions(cm)
22.9 x 15.2

Hardcover

$661.75

or 4 interest-free payments of $165.44 with

Available: 1st October 2026

Preorder. Will ship when available.

Machine Learning Solutions for Inverse Problems: Part B, Volume 27 in the Handbook of Numerical Analysis, continues the exploration of emerging approaches at the intersection of machine learning and inverse problem theory. This volume presents a collection of chapters addressing a wide range of contemporary topics, including deep image prior methods for computed tomography, data-consistent learning strategies, and unified frameworks for training and inversion in machine learning-based reconstruction methods. Additional chapters examine learned regularization techniques, generative models for inverse problems, and the integration of deep learning with traditional computational frameworks such as full waveform inversion and PDE-based inverse modeling. The volume also discusses advances in self-supervised learning, data selection strategies, plug-and-play denoising methods, and diffusion models for solving imaging inverse problems. Further contributions explore neural network representations, operator learning, and learned iterative schemes, along with theoretical perspectives on stability, approximation hardness, hallucinations, and trustworthiness in AI-driven inverse problem methodologies.

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