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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#inverseproblems
Latest posts tagged with #inverseproblems on Bluesky
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Posts tagged #inverseproblems
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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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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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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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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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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Stratospheric Aerosol Source Inversion: Noise, Variability, and Uncertainty Quantification
www.dl.begellhouse.com/journals/558...
#AerosolSourceInversion #InverseProblems #E3SMModel
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🦾 #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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One-sentence proof of McLaughlin and Rundell's inverse uniqueness theorem
#spectraltheory #Schrödinger #inverseproblems
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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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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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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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🧠🩻 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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"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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🚀 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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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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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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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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“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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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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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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