Inverse Problems

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Overview

Inverse problems work backward from observations to estimate the hidden image, physical state, or system parameters that produced them, often when measurements are sparse, noisy, or ambiguous. Methods range from classical regularization and optimization to physics-informed neural networks, learned operators, and generative priors such as diffusion and flow models; Bayesian methods can also represent multiple plausible solutions and their uncertainty. Applications include medical and computational imaging, seismic exploration, spectroscopy, and identifying material or dynamical properties from sensor data. A central challenge is balancing measurement consistency with useful prior information: learned priors can recover detail but may invent unsupported structure, especially when the forward model or data differs from training, so identifiability, stability, and calibrated uncertainty remain important.

Latest papers

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245 papers30 papers in the last four weeks, up 36% on the four weeks before. 0.2% of all new papers.

  1. Multi-Solution Inverse Design of Pixelated Patch Antennas Using an Autoregressive Transformer

    Oct 29, 2026Young-Mok Yun, Seung-Won Shin, Jaeyong Chung +1Inverse DesignInverse Problems

    Department of System Semiconductor Engineering, Yonsei University, Seoul 03722, South Korea.

  2. The proton structure one mode at a time: a Neural Tangent Kernel analysis of a DIS fit

    Oct 26, 2026Amedeo Chiefa, Luigi Del Debbio, Richard KenwayNeural Network Training DynamicsNeural Tangent Kernel

    The Higgs Centre for Theoretical Physics, School of Physics and Astronomy, The University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD, United Kingdom

  3. Sparse Reconstruction by Direct Measurement-to-Field Generation in Function Space

    Oct 26, 2026Linzheng Wang, Jason Chen, Nicolas Tricard +3Sparse RecoveryInverse Problems

    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Department of Civil, Environmental and Geomatic Engineering, University College London, London WC1E 6BT, UK · Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK

  4. Beyond Best-of-kk: Prior-Consistent DAPS for Ambiguous Nonlinear Inverse Problems

    Oct 22, 2026Merve Kocabas, Benjamin A. Burns, Andreas Geiger +2Diffusion-Based Inverse ProblemsInverse Problems

    University of Tübingen · Zuse School ELIZA · Max Planck Institute for Intelligent Systems +4

  5. Compositional neural networks for multimodal X-ray and neutron scattering inversion

    Oct 21, 2026Valentin Munteanu, Alexander Hinderhofer, Dmitry Lapkin +2Inverse ProblemsUncertainty-Aware Multimodal Fusion

    Institute of Applied Physics, University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany · Cluster of Excellence ”Machine Learning” - new perspectives for science, University of Tübingen, Germany

  6. Diffusion Posterior Sampling with Uncertain Forward Models

    Oct 21, 2026Wentao Shangguan, Vivek K GoyalDiffusion-Based Inverse ProblemsDiffusion Posterior Sampling

    Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA

  7. Predicting silent failures of learned inference under restricted access: what measurement physics and prior information explain

    Oct 20, 2026Mario GianniInverse ProblemsBayesian Inverse Problems

    School of Computer Science and Informatics, University of Liverpool, UK

  8. PnP-CF: A Consensus Flow-Matching Solver for General Inverse Problems

    Oct 19, 2026Dibyanshu Kumar, Magda GregorovaInverse ProblemsFlow Matching

    TH Würzburg-Schweinfurt, CAIRO, University of Würzburg

  9. Mixed Poisson diffusion models and application to posterior sampling

    Oct 14, 2026Thibaut Pellerin, Alain Durmus, Elinor Berger +1Diffusion ModelsDiffusion Posterior Sampling

    CMAP, CNRS, École polytechnique, Institut Polytechnique de Paris, Palaiseau, France · Université Paris-Saclay, CEA, List, F-91120 Palaiseau, France

  10. Structure-aware Bayesian full waveform inversion via diffusion-regularized Stein variational gradient descent

    Oct 13, 2026Chao Li, Mrinal K. Sen, Sergey Fomel +1Seismic ImagingBayesian Inverse Problems

    The University of Texas at Austin

  11. DiffGIPC: Differentiable Mixed-Coordinate Contact Dynamics on GPU

    Oct 13, 2026Kemeng Huang, Bowen Ren, Xinyu Lu +2Differentiable PhysicsInverse Problems

    The University of Hong Kong, Hong Kong SAR, China · TransGP, Hong Kong SAR, China · Carnegie Mellon University, USA

  12. Over-parameterization Is Neither Robust nor Fragile in Matrix Sensing: A Geometric Theory of Solution Robustness

    Oct 13, 2026Runze Shi, Ziye MaOverparameterizationMatrix Factorization

    City University of Hong Kong

  13. Physical-Field-Bridged Material Parameter Inference from Multi-View Deformation Videos

    Oct 13, 2026Linrui Shou, Jie Li, Yu Liu +1Materials Property PredictionInverse Problems

    Shanghai Jiao Tong University · University of Science and Technology Beijing · Peking University

  14. Taming Nonconvexity with Epigraph Lifting

    Oct 12, 2026Clara Lage, Hok Shing Wong, Matthias EhrhardtNonconvex OptimizationInverse Problems

    Institut Camille Jordan, École Centrale de Lyon · LMU Munich, Munich Center for Machine Learning · University of Bath

  15. Full Waveform Inversion with a Learned Preconditioner

    Oct 11, 2026Jeongjin ParkSeismic ImagingInverse Problems

  16. Inverse Synthesis of Shock Time Series from Shock Response Spectra Using Ensembles of Conditional Variational Autoencoders

    Oct 9, 2026Adam Watts, Charles Young, Ryan Bowering +2Inverse ProblemsConditional Variational Autoencoder

    Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM 87545, USA · University of Rochester, Rochester, NY, USA · 241 18th Street South, Suite 650, Arlington, VA 22202

  17. Edge Accuracy Is Not Enough: Why Dynamics-Learned Structure Fails to Transfer to Inverse Problems

    Oct 7, 2026Nicholas Tan Jerome, Fangnian WangInverse ProblemsGraph Structure Learning

    Institute for Data Processing and Electronics (IPE) Karlsruhe Institute of Technology (KIT) Karlsruhe, Germany · Institute for Thermal Energy Technology and Safety (ITES) Karlsruhe Institute of Technology (KIT) Karlsruhe, Germany

  18. Fast holographic inversion of superconducting domes

    Oct 7, 2026Sejin KimInverse ProblemsQuantum Many-Body Physics

    Center for AI and Natural Sciences, Korea Institute for Advanced Study, Seoul 02455, Korea

  19. Self-attention summary networks for subsurface velocity-model building from common-image gathers

    Oct 7, 2026Shiqin Zeng, Yunlin Zeng, Abhinav Prakash Gahlot +2Seismic ImagingInverse Problems

    Georgia Institute of Technology

  20. Twist Flow for Inverse Problems

    Oct 7, 2026Shiqin Zeng, Zijun Deng, Felix J. HerrmannBayesian Inverse ProblemsPosterior Sampling

    Georgia Institute of Technology

  21. Inferring physical fields in coupled systems with unknown parameters from incomplete observations using physics-constrained attentive neural operators

    Oct 5, 2026Shilun Wei, Xiaoqiang Sun, Wei Li +1Parameter IdentifiabilityPhysics-Informed Neural Operators

    School of Mathematics, Sun Yat-sen University, Guangzhou, China · School of Sciences, Great Bay University, Dongguan, China · John Hopcroft Center for Computer Science, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China

  22. Robust Ensemble Guidance for Scientific Inverse Problems

    Oct 4, 2026Zixiang Li, Wei Wang, Yunchao Wei +2Diffusion Model GuidanceDiffusion-Based Inverse Problems

    Beijing Jiaotong University · Tsinghua University

  23. R1A-PC: Physics-Guided Electromagnetic Inversion of Three-Dimensional Human Point Clouds in Complex Static Environments

    Oct 4, 2026Xudong Yuan, Ruyun Xu, Jingtai Yang +13D ReconstructionPoint Cloud Surface Reconstruction

    School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun, China.

  24. A Contrast-Source Inversion Scheme Based on Stochastic Optimization and Plug-and-Play Regularization

    Oct 4, 2026Lingqi Gao, Hakan BagciInverse Problems

    Electrical and Computer Engineering (ECE) Program Computer, Electrical, and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal, 23955-6900, Saudi Arabia

  25. A differentiable Lagrangian-coupled 3D Gaussian Splatting-SPH model for forward simulation and inverse analysis in solid mechanics

    Oct 3, 2026Tian Xu, Soroush Atashi, Tianju XueDeformable Object SimulationStructural Mechanics

    Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology

  26. LyapuFlow: Controlling Generative Flows with Lyapunov Feedback for Inverse Problems

    Oct 3, 2026Minseon Gwak, Hans Hao-Hsun Hsu, Danielle C. Maddix +1Constrained Generative ModelingInference-Time Guidance

    Lawrence Berkeley National Lab · International Computer Science Institute · Georgia Institute of Technology +1

  27. Dynamic Time Step Prediction in Inverse Heat Dissipation for Blur-Like Image Restoration Tasks

    Oct 2, 2026Cap Dang Xuan Kiet, Tat-Jen ChamDiffusion Models for Image RestorationDiffusion Model Inversion

    Nanyang Technological University Singapore

  28. The Neuro-Physical Inverter: A Modular Framework for Magnetotelluric Inversion Coupling Ensemble Conditioning with Residual Learning

    Oct 2, 2026Jae Deok Kim, Sai Ravela, Rob. L. EvansInverse ProblemsUncertainty Quantification

    MIT-WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering, Cambridge, MA 02139 USA · Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA · Department of Geology and Geophysics, Woods Hole Oceanographic Institution, Woods Hole, MA 02543 USA

  29. One Photon, Many Worlds: Posteriors and Predictions with Single-Photon Cameras

    Oct 2, 2026Haejoon Lee, Mohit Gupta, Vijayakumar Bhagavatula +1Computational ImagingInverse Problems

    Carnegie Mellon University · Carnegie Mellon University, Pittsburgh, PA, USA · University of Wisconsin-Madison +1

  30. Initial condition recovery in nonlinear damped viscous photoacoustic tomography using a convolutional neural network-guided gradient-free optimization framework

    Oct 1, 2026Madhu Gupta, Anwesa Dey, Prapti Tala +1Medical ImagingGradient-Free Optimization

    Department of Mathematics, Indian Institute of Technology, Gandhinagar, Gujarat, India. · Department of Mathematics, University of Utah, Salt Lake City, Utah, UT, USA. · Department of Mathematics, The University of Texas at Arlington, Arlington, TX, USA.