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bubba t data driven models inverse problems 2025

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Textbook in PDF format Advances in learning-based methods are revolutionizing several fields in applied mathematics, including inverse problems, resulting in a major paradigm shift towards data-driven approaches. This volume, which is inspired by this cutting-edge area of research, brings together contributors from the inverse problem community and shows how to successfully combine model- and data-driven approaches to gain insight into practical and theoretical issues. Preface Mathematical aspects of data-driven methods in inverse problems On optimal regularization parameters via bilevel learning Learned regularization for inverse problems Inverse problems with learned forward operators Unsupervised approaches based on optimal transport and convex analysis for inverse problems in imaging Learned reconstruction methods for inverse problems: sample error estimates Statistical inverse learning problems with random observations General regularization in covariate shift adaptation Applications of data-driven methods in inverse problems Analysis of generalized iteratively regularized Landweber iterations driven by data Integration of model- and learning-based methods in image restoration Dynamic computerized tomography using inexact models and motion estimation Deep Bayesian inversion Utilizing uncertainty quantification variational autoencoders in inverse problems with applications in photoacoustic tomography Electrical impedance tomography: a fair comparative study on deep learning and analytic-based approaches Classification with neural networks with quadratic decision functions Index

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bubba t data driven models inverse problems 2025

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