Inverse Problems

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20 papers in the last four weeks, up 186% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 161

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P-Flow: Proxy-gradient Flows for Linear Inverse Problems
May 8, 2026Zehua Jiang, Fenghao Zhu, Xinquan Wang +2Flow Matching Inverse Problems
University of Notre Dame Indiana, USA
PyCC.id: A package for hypothesis-driven equation discovery with structural identifiability
May 7, 2026Federico J. GonzalezAutonomous Scientific Discovery Inverse Problems
Physics Institute of Rosario (IFIR), CONICET-UNR, Blvd. 27 de Febrero 210 Bis, Rosario, S2000EZP, Argentina.
Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees
May 6, 2026Ahmad Aghapour, Erhan Bayraktar, Asaf CohenDiffusion Models for Image Restoration Langevin Dynamics
Department of Mathematics University of Michigan Ann Arbor, MI 48109, USA
DualTCN: A Physics-Constrained Temporal Convolutional Network for 2 Time-Domain Marine CSEM Inversion
May 6, 2026Khaled Ahmed, Ghada OmarTemporal Convolutional Networks Physics-Informed ML
School of Computing, Southern Illinois University Carbondale, Carbondale, IL, 62901, USA · School of Mathematics and Statistics, Southern Illinois University Carbondale, Carbondale, IL, 62901, USA
Conditions for well-posed color recovery in scattering media
May 5, 2026Grigory Solomatov, Derya AkkaynakImage Restoration Inverse Problems
Hatter Department of Marine Technologies, University of Haifa, Haifa, Israel · Interuniversity Institute for Marine Sciences, Eilat, Israel
Tempered Guided Diffusion
May 5, 2026Andreas Makris, Paul Fearnhead, Chris NemethDiffusion Model Sampling Diffusion-Based Inverse Problems
Department of Mathematics and Statistics Lancaster University, UK · Department of Mathematics and Statistics2026 Lancaster University, UK
Meta-Inverse Physics-Informed Neural Networks for High-Dimensional Ordinary Differential Equations
May 5, 2026Zhao Wei, Kenneth Hor Cheng Koh, Sheng Yuan Chin +3Meta-Learning Physics-Informed ML
Centre for Frontier AI Research (CFAR) and the Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore · Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Nanos, Singapore 138669, Singapore · A*STAR Skin Research Labs (A*SRL), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos, Singapore 138648, Singapore +1
Neural Posterior Estimation of Terrain Parameters from Radar Sounder Data
May 5, 2026Jordy Dal Corso, Annalena Kofler, Marco Cortellazzi +2Simulation-Based Inference Parameter Estimation
University of Trento, Trento, Italy · Max Planck Institute for Intelligent Systems, Tübingen, Germany · Max Planck Institute for Gravitational Physics (Albert Einstein Institute), Potsdam, Germany +1
Hearing the Room Through the Shape of the Drum: Modal-Guided Sound Recovery from Multi-Point Surface Vibrations
Apr 29, 2026Shai Bagon, Matan Kichler, Mark SheininInverse Problems
Weizmann Institute of Science, Israel
Distill-Belief: Closed-Loop Inverse Source Localization and Characterization in Physical Fields
Apr 28, 2026Yiwei Shi, Zixing Song, Mengyue Yang +2Belief-Space Planning Reward Hacking
SEMT, University of Bristol, Bristol, UK · LUCAS, Loughborough University, Loughborough, UK
Adaptable phase retrieval for coherent transition radiation spectroscopy based on differentiable physics information
Apr 28, 2026Ritz Ann Aguilar, Maxwell LaBerge, Andreas Doepp +6Differentiable Physics Physics-Informed ML
Institute of Radiation Physics, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany · Centre for Advanced Laser Applications, Ludwig-Maximilians-Universität München, Garching, Germany · Center for Advanced Systems Understanding, Görlitz, Germany +2
When Less Is More: Simplicity Beats Complexity for Physics-Constrained InSAR Phase Unwrapping
Apr 28, 2026Prabhjot Singh, Manmeet SinghU-Net Remote Sensing
The University of Texas at Austin, USA · RediMinds Inc., USA · Western Kentucky University, USA
Learning Interpretable PDE Representations for Generative Reconstructions with Structured Sparsity
Apr 26, 2026Valerie Tsao, Nathaniel Chaney, Manolis VeveakisPhysics-Informed Diffusion Models Sparse Recovery
Department of Civil & Environmental Engineering, Duke University, NC, USA.
On the Existence of an Inverse Solution for Preference-Based Reductions in Argumentation
Apr 24, 2026Alessio Zaninotto, Bruno Yun, Nir Oren +1Computational Argumentation Inverse Problems
1CRIL - Université d’Artois, CNRS · 2Universite Claude Bernard Lyon 1, CNRS, Ecole Centrale de Lyon, INSA Lyon, Université Lumière Lyon 2, LIRIS, UMR5205, 69622 Villeurbanne, France · University of Aberdeen
Multimodal Diffusion to Mutually Enhance Polarized Light and Low Resolution EBSD Data
Apr 24, 2026Harry Dong, Timofey Efimov, Megna Shah +4Diffusion Models for Image Restoration Materials Science
Department of Electrical and Computer Engineering, Carnegie Mellon University, USA · Materials and Manufacturing Directorate, Air Force Research Laboratory, USA · Department of Materials Science and Engineering, Carnegie Mellon University, USA +1
Boltzmann Machine Learning with a Parallel, Persistent Markov chain Monte Carlo method for Estimating Evolutionary Fields and Couplings from a Protein Multiple Sequence Alignment
Apr 20, 2026Sanzo MiyazawaSequence Modeling Inverse Problems
Self-Supervised Angular Deblurring in Photoacoustic Reconstruction via Noisier2Inverse
Apr 17, 2026Markus Haltmeier, Nadja Gruber, Gyeongha HwangMedical Imaging Image Deblurring
Department of Mathematics, University of Innsbruck Technikerstrasse 13, 6020 Innsbruck, Austria · Department of Computer Science, University of Innsbruck Technikerstrasse 21a, 6020 Innsbruck, Austria · Department of Mathematics, Yeungnam University 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, South Korea
SPLIT: Self-supervised Partitioning for Learned Inversion in Nonlinear Tomography
Apr 17, 2026Markus Haltmeier, Lukas Neumann, Nadja Gruber +1Self-Supervised Learning Sparse-View CT Reconstruction
IRIS: A Real-World Benchmark for Inverse Recovery and Identification of Physical Dynamic Systems from Monocular Video
Mar 17, 2026Rasul Khanbayov, Mohamed Rayan Barhdadi, Erchin Serpedin +1Parameter Identifiability System Identification
Hamad Bin Khalifa University · Texas A&M University
Joint 3D Gravity and Magnetic Inversion via Rectified Flow and Ginzburg-Landau Guidance
Mar 6, 2026Dhruman Gupta, Yashas Shende, Aritra Das +2Physics-Informed ML Inverse Problems
Ashoka University · Department of Computer Science Ashoka University · Department of Physics Ashoka University
Inverse Reconstruction of Shock Time Series from Shock Response Spectrum Curves using Machine Learning
Mar 3, 2026Adam Watts, Andrew Jeon, Destry Newton +1Inverse Problems
Los Alamos National Laboratory · University of Rochester
Separating Oblivious and Adaptive Models of Variable Selection
Feb 18, 2026Ziyun Chen, Jerry Li, Kevin Tian +1Sparse Recovery Feature Selection
University of Washington · University of Texas at Austin
Stability and Concentration in Nonlinear Inverse Problems with Block-Structured Parameters: Lipschitz Geometry, Identifiability, and an Application to Gaussian Splatting
Feb 10, 2026Joe-Mei Feng, Hsin-Hsiung KaoInverse Rendering Parameter Identifiability
Computer Science and Information Engineering, Tamkang University, New Taipei City, Taiwan (R.O.C.) · Department of Information Management, Central Police University, Taoyuan, Taiwan
Trajectory Stitching for Solving Inverse Problems with Flow-Based Models
Feb 9, 2026Alexander Denker, Zeljko Kereta, Carola-Bibiane Schönlieb +1Generative Flow Networks Flow-Based Generative Modeling
Department of Computer Science, University College London · Department of Applied Mathematics and Theoretical Physics, University of Cambridge.
Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification
Feb 3, 2026Jack Michael Solomon, Rishi Leburu, Matthias ChungVariational Autoencoders Uncertainty Quantification
Emory University Department of Mathematics
Inverse Problems Conditioned on Observation Ensembles: Applications and Methods
Jan 29, 2026Zhengyan Huan, Camila Pazos, Martin Klassen +3Posterior Sampling Inverse Problems
Department of Electrical and Computer Engineering, Tufts University, Medford, Massachusetts · The NSF AI Institute for Artificial Intelligence and Fundamental Interactions · Department of Physics and Astronomy, Tufts University, Medford, Massachusetts +1
Fundamental Recovery Bounds for SPAD Signals under Stationary Flux
Jan 12, 2026Lior Dvir, Nadav Torem, Mohit Gupta +1Bayesian Inverse Problems Diffusion Posterior Sampling
Viterbi Faculty of Electrical and Computer Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel · Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem
Dec 5, 2025Yeobin Hong, Suhyeon Lee, Hyungjin Chung +1Camera-Controlled Video Generation Video Diffusion Models
Graduate School of AI, KAIST, South Korea · EverEx, South Korea · Korea University, South Korea
Flexible Gravitational-Wave Parameter Estimation with Transformers
Dec 2, 2025Annalena Kofler, Maximilian Dax, Stephen R. Green +6Transformer Inference Gravitational-Wave Astronomy
Max Planck Institute for Intelligent Systems, Max-Planck-Ring 4, 72076 Tübingen, Germany · ELLIS Institute Tübingen, Maria-von-Linden-Straße 2, 72076 Tübingen, Germany · Tübingen AI Center, Maria-von-Linden-Straße 1, 72076 Tübingen, Germany +4
Physics-Informed Neural Networks for Speech Production
Nov 1, 2025Kazuya Yokota, Ryosuke Harakawa, Masaaki Baba +1Speech Processing Physics-Informed ML
Department of Mechanical Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, Niigata, Japan · Department of Electrical, Electronics and Information Engineering, Nagaoka University of Technology

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P-Flow: Proxy-gradient Flows for Linear Inverse Problems

Zehua Jiang, Fenghao Zhu, Xinquan Wang, Chongwen Huang+1

University of Notre Dame Indiana, USA

Flow Matching Inverse Problems Image Inverse Problems

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Generative models based on flow matching have emerged as a powerful paradigm for inverse problems, offering straighter trajectories and faster sampling compared to diffusion models. However, existing approaches often necessitate differentiating through unrolled paths, leading to numerical instability and prohibitive computational overhead. To address this, we propose P-Flow, a framework that stabilizes the reconstruction process by leveraging a proxy gradient to update the source point. This approach effectively circumvents the numerical instability and memory overhead of long-chain differentiation. To ensure consistency with the prior distribution, we employ a Gaussian spherical projection motivated by the concentration of measure phenomenon in high-dimensional spaces. We further provide a theoretical analysis for P-Flow based on Bayesian theory and Lipschitz continuity. Experiments across diverse restoration tasks demonstrate that P-Flow delivers competitive performance, especially under extreme degradations such as severely ill-posed conditions and high measurement noise.

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