#InverseProblems

@arxivlens.bsky.social

5 months ago

Learning Regularization Functionals for Inverse Problems: A Comparative
Study
Alexander Denker, Carola-Bibiane Schönlieb et al.
Paper
Details
#InverseProblems #RegularizationFunctionals #ComparativeStudy

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GetNews.me

@getnews-me.bsky.social

5 months ago

Data Selection in PDE Inverse Problems Meets Randomized Linear Algebra

RNLA provides probabilistic guarantees—at least 1‑p chance of staying within tolerance—while sketching PDE inverse problems to cut data size and keep key information. Read more: getnews.me/data-selection-in-pde-in... #pde #inverseproblems

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GetNews.me

@getnews-me.bsky.social

5 months ago

Stability Bounds for Unfolded Forward‑Backward Neural Networks

The paper derives analytical Lipschitz constants that bound how an unfolded forward‑backward network’s output changes with small input perturbations; it was submitted on 23 Dec 2024 (v1). getnews.me/stability-bounds-for-unf... #inverseproblems #stability

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Gltch

@gltch.io

5 months ago

AI techniques excel at solving complex equations in physics, especially inverse problems Differential equations are fundamental tools in physics: they are used to describe phenomena ranging from fluid dynamics to general relativity. But when these equations become stiff (i.e. they involve very different scales or highly sensitive parameters), they become extremely difficult to solve. This is especially relevant in inverse problems, where scientists try to deduce unknown physical laws from observed data.

Physics' deepest laws are coded in equations…but what if those equations fight back? 🤯 Solving them unlocks universes, even hidden ones! ✨ #InverseProblems

Source: phys.org/news/2025-10-ai-techniqu...

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GetNews.me

@getnews-me.bsky.social

5 months ago

Convergence Study Expands Inverse Problem Regularization Techniques

Researchers introduced implicit non‑variational (INV) regularization, proving stability and convergence for deep equilibrium (DEQ) and plug‑and‑play (PnP) methods. Read more: getnews.me/convergence-study-expand... #inverseproblems #deepequilibrium

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STRUCTURES Cluster of Excellence Heidelberg

@structures-hd.bsky.social

6 months ago

The Schöntal workshop was thus a lively and enriching experience!

The workshop is funded by the STRUCTURES YRC and it celebrated its 9th edition this year. 2/2

#Regularization #BayesianMethods #InverseProblems #Physics #Mathematics #Schöntal

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Journal of Machine Learning for Modeling and Computing

@jmlmc.bsky.social

7 months ago

Stratospheric Aerosol Source Inversion: Noise, Variability, and Uncertainty Quantification

www.dl.begellhouse.com/journals/558...

#AerosolSourceInversion #InverseProblems #E3SMModel

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IOPP Machine Learning and AI

@iopp-mlresearch.bsky.social

8 months ago

🦾 #InverseProblems meet #MachineLearning: Stochastic optimization techniques, born in the era of big data, are now revolutionizing variational regularization in imaging.

Read the full article here: bit.ly/4n1tUTc

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Namig J. Guliyev

@njguliyev.bsky.social

9 months ago

(PDF) On the McLaughlin–Rundell theorem PDF | We give a one-sentence proof of McLaughlin and Rundell's inverse uniqueness theorem. | Find, read and cite all the research you need on ResearchGate

One-sentence proof of McLaughlin and Rundell's inverse uniqueness theorem

#spectraltheory #Schrödinger #inverseproblems

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Journal of Machine Learning for Modeling and Computing

@jmlmc.bsky.social

9 months ago

Parameter Estimation for the Reduced Fracture Model via a Direct Filter Method

www.dl.begellhouse.com/journals/558...

#FractureMechanics #ParameterEstimation #ComputationalModeling #InverseProblems

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Begell Engineering Research

@begellengineering.bsky.social

9 months ago

Parameter Estimation for the Reduced Fracture Model via a Direct Filter Method

www.dl.begellhouse.com/journals/558...

#FractureMechanics #ParameterEstimation #ComputationalModeling #InverseProblems

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Begell House Inc. Publishers

@begellhouse.bsky.social

9 months ago

Parameter Estimation for the Reduced Fracture Model via a Direct Filter Method

www.dl.begellhouse.com/journals/558...

#FractureMechanics #ParameterEstimation #ComputationalModeling #InverseProblems

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neuriverse

@neuriverse.bsky.social

10 months ago

An incremental algorithm for non-convex AI-enhanced medical image processing Solving non-convex regularized inverse problems is challenging due to their complex optimization landscapes and multiple local minima. However, these models remain widely studied as they often yield high-quality, task-oriented solutions, particularly in medical imaging, where the goal is to enhance clinically relevant features rather than merely minimizing global error. We propose incDG, a hybrid framework that integrates deep learning with incremental model-based optimization to efficiently approximate the $\ell_0$-optimal solution of imaging inverse problems. Built on the Deep Guess strategy, incDG exploits a deep neural network to generate effective initializations for a non-convex variational solver, which refines the reconstruction through regularized incremental iterations. This design combines the efficiency of Artificial Intelligence (AI) tools with the theoretical guarantees of model-based optimization, ensuring robustness and stability. We validate incDG on TpV-regularized op

🧠🩻 new ai-powered method blends deep learning with model-based optimisation to tackle tough non-convex problems for sharper, more stable medical imaging results
https://arxiv.org/abs/2505.08324v1
#medicalimaging #deeplearning #inverseproblems #ai #reconstruction

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Namig J. Guliyev

@njguliyev.bsky.social

10 months ago

Spectral identities for Schrödinger operators | Canadian Mathematical Bulletin | Cambridge Core Spectral identities for Schrödinger operators - Volume 68 Issue 2

"Spectral identities for Schrödinger operators" has now been published in the paginated issue. #OpenAccess @cambridgeup.bsky.social

doi.org/10.4153/S000...

#spectraltheory #Schrödinger #inverseproblems

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Dagmar Iber & CoBi

@iberd.bsky.social

11 months ago

🚀 Introducing #PINNverse — a game-changer for parameter estimation in differential equations! 🧠💡

No forward solves. Better accuracy. Robust to noise.

Preprint: doi.org/10.48550/arX...

#SciComm #MachineLearning #InverseProblems #PINNs

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Planetary Ecologist

@planetaryecologist.mastodon.social.ap.brid.gy

11 months ago

Original post on mastodon.social

Optimal estimation (Remote sensing 🛰️)

In applied statistics, optimal estimation is a regularized matrix inverse method based on Bayes' theorem. It is used very commonly in the geosciences, particularly for atmospheric sounding. A matrix inverse problem looks like this: A x → = y → […]

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Helmholtz Imaging

@helmholtzimaging.bsky.social

1 year ago

Samira Kabri from the HI Research Unit at DESY giving a talk at a school within the program "Wir wollen's wissen".

Did you know a CT scan uses math to create images from X-ray data? 🤔 Prof. Martin Burger and Samira Kabri from our Research Unit at #DESY shared how #inverseproblems turn data into images during "Wir wollen’s wissen" at Hamburg schools. Inspiring future scientists! 💡

@uni-hamburg.de

#science

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Helmholtz Imaging

@helmholtzimaging.helmholtz.social.ap.brid.gy

1 year ago

Samira Kabri from the HI Research Unit at DESY giving a talk at a school within the program "Wir wollen's wissen".

Did you know a CT scan uses math to create images from X-ray data? 🤔 Prof. Martin Burger and Samira Kabri from our Research Unit at @DESYnews shared how #inverseproblems turn data into images during "Wir wollen’s wissen" at Hamburg schools. Inspiring […]

[Original post on helmholtz.social]

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Namig J. Guliyev

@njguliyev.bsky.social

1 year ago

Inverse square singularities and eigenparameter-dependent boundary conditions are two sides of the same coin Abstract. We show that inverse square singularities can be treated as boundary conditions containing rational Herglotz–Nevanlinna functions of the eigenval

“Inverse square singularities and eigenparameter dependent boundary conditions are two sides of the same coin” can be freely accessed at
academic.oup.com/qjmath/artic...

#spectraltheory #inverseproblems #Bessel #Darboux #Schrödinger #supersymmetry

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International Society for Bayesian Analysis

@isba-bayesian.bsky.social

1 year ago

Clusterization in D-optimal Designs: The Case Against Linearization Estimation of parameters in physical processes often demands costly measurements, prompting the pursuit of an optimal measurement strategy. Finding such strategy is termed the problem of optimal exper...

Advance BA pub:

In #inverseproblems, optimal sensor locations may be clustered. Daon shows that clusterization is a consequence of the pigeonhole principle and generic for linear problems, arguing against linearization when seeking optimal measurement locations.

projecteuclid.org/journals/bay...

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lebellig

@lebellig.bsky.social

1 year ago

Currently reading the survey on diffusion models for inverse problems by G.Daras et al. and it's very well written. I definitely needed an update since the last article I read on this specific subject was DDRM and I truly enjoy this reading. #diffusion #inverseproblems

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Posts tagged #InverseProblems