Operator Learning

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12 papers in the last four weeks, up 200% on the four weeks before. 0.1% of all new papers.

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  1. Langevin-Informed Transfer Learning: Replacing Target Samples by Black-Box Feedback

    Oct 1, 2026Vladimir R. Kostic, Karim Lounici, Hélène Halconruy +3Latent Dynamics ModelingTransfer Learning

    CSML, Istituto Italiano di Tecnologia · University of Novi Sad · CMAP-Ecole Polytechnique +5

  2. SupraTITO: Transferable Generative Molecular Dynamics for Supramolecular Systems

    Oct 1, 2026Weilong Chen, Nuno Costa, Julija ZavadlavOperator LearningMolecular Dynamics Simulation

    MFM Lab, TUM, Germany · AMC, TUM, Germany

  3. Attention Kernels for Learning Maps Between Heavy-Tailed Measures

    Sep 30, 2026Kailen Hargenrader, Edoardo Calvello, Bohan ChenSoftmax AttentionAttention Mechanisms

    Computing and Mathematical Sciences California Institute of Technology · Lawrence Berkeley National Laboratory University of California, Berkeley ICSI

  4. Fractional Laplace Neural Operators: Exact Architectures, an Expressivity Frontier at Criticality, and Certified Stability for Memory-Driven Network Dynamics

    Sep 30, 2026Mauricio Herrera-MarínOperator LearningNeural Operators

    Facultad de Ingenier´ıa, Universidad del Desarrollo, Santiago, Chile

  5. Super-Resolving Unseen Hyperspectral Sensors at Any Scale via Spatial Operators

    Sep 30, 2026Ji-Xuan He, Guohang Zhuang, Bo Junge +6Hyperspectral Image Super-ResolutionOperator Learning

    Xi’an Jiaotong University · Hefei University of Technology · National University of Singapore +1

  6. Learning Conditional Expectation Operators via Functional Newton Updates

    Sep 28, 2026Thiago Ramos, Alek Fröhlich, Daniel Perazzo +1Density Ratio EstimationLow-Rank Approximation

    Federal University of São Carlos · CSML, Istituto Italiano di Tecnologia and University of Genoa · CSML, Istituto Italiano di Tecnologia and University College London

  7. Universal Approximation of Measure-to-Measure Operators by Pushforwards

    Sep 28, 2026Takashi Furuya, Nicholas H. Nelsen, Frank ColeTransformerUniversal Approximation

    Doshisha University, RIKEN AIP · UT Austin · UCLA

  8. Adapting neural operators for mechanics decisions under changing operating conditions

    Sep 27, 2026Prashant K. Jha, Koffi Enakoutsa, Ian Galloway +1Neural Surrogate ModelingSurrogate-Assisted Optimization

    Department of Mechanical Engineering, South Dakota School of Mines and Technology, Rapid City, SD 57701, USA · Department of Mathematics, University of California, Los Angeles, Los Angeles, CA 90095, USA

  9. Neural Scaling Laws of Transformer Operator Network

    Sep 27, 2026Haoran Yan, Zhongjie Shi, Yuanzhe Xi +2Neural Network GeneralizationNeural Network Approximation Theory

    School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332, USA · Department of Mathematics, Emory University, Atlanta, GA 30322, USA · School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA

  10. MENO: Memory-Efficient Neural Operator

    Sep 23, 2026Shengyang Xu, Weijun Zhang, Jun Hu +1PDE Surrogate ModelingPDE Operator Learning

    School of Mathematical Sciences, Peking University, Beijing 100871, China · Chongqing Research Institute of Big Data, Peking University, Chongqing 401329, China · National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing 100871, China

  11. Demystifying Linear Operator Learning for Control Systems

    Sep 16, 2026Max Beier, Nicolas Hoischen, Sandra Hirche +1System IdentificationOperator Learning

    Chair of Information-Oriented Control, School of Computation, Information and Technology, Technical University of Munich, 80333 Munich, Germany

  12. Deep operator learning for efficient sampling from invariant measures of stochastic differential equations

    Sep 10, 2026Ling Guo, Lei Li, Jingtong ZhangStochastic Differential EquationsDiffusion Sampling

    Department of Mathematics, Shanghai Normal University, Shanghai, China · School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China · Institute of Natural Sciences, MOE-LSC, Shanghai Jiao Tong University, Shanghai, China

  13. Equation Recast for Canonical Operator Learning Across Parametric PDEs

    Sep 2, 2026Qiyun Cheng, Valentin Duruisseaux, Cesar F. Clauser +7PDE Operator LearningOperator Learning

    1Plasma Science and Fusion Center, Massachusetts Institute of Technology, Cambridge, MA, USA · 2Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA · Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA +1

  14. The Frame Kernel Method for Multiscale Operator Learning

    Aug 25, 2026Branden Frieden, Ryan Whitehead, M. Keith Ballard +2PDE Surrogate ModelingKernel Methods

    Kahlert School of Computing, University of Utah, Salt Lake City, UT 84112, USA. · U.S. Air Force Research Laboratory, Wright-Patterson AFB, OH 45433, USA.

  15. Kernel Methods for Learning Operators with Multiple Inputs and Outputs

    Aug 12, 2026Adrien Weihs, Chunyang Liao, Jingmin Sun +1Kernel MethodsPDE Operator Learning

    Department of Mathematics, University of California Los Angeles, Los Angeles, CA 90095, USA. · Department of Mathematical Sciences, University of Arkansas, Fayetteville, AR, 72701, USA. · Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.

  16. Two-Step MV-DeepONet: Probabilistic Operator Learning for Uncertainty Propagation Driven by Random Input Fields

    Aug 10, 2026Yupei Nie, Lei Wang, Jiasen LiuUncertainty QuantificationPDE Surrogate Modeling

    School of Mathematics and Physics, North China Electric Power University, Beijing 102206, P.R. China · School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, P.R. China

  17. A foundation model of numerical intelligence with cross-disciplinary generalization

    Jul 30, 2026Chenghan Wu, Zongmin Yu, Liu YangNumerical Reasoning in Language ModelsDomain Generalization

    Department of Mathematics, National University of Singapore, Singapore, Singapore.

  18. Neural operator discovery from heterogeneous trajectories

    Jul 25, 2026Zituo Chen, Qiaofeng Li, Jiaxin Hu +1Latent Dynamics ModelingNonlinear System Identification

    Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA. · State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, Zhejiang Province, China.

  19. Near-Optimal Learning of Gaussian Sobolev Operators

    Jul 8, 2026Ben Adcock, Michael Griebel, Gregor MaierRecursive Least SquaresOperator Learning

    Department of Mathematics, Simon Fraser University, Burnaby BC, Canada · Institute for Numerical Simulation, University of Bonn, Bonn, Germany · Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany

  20. Hybrid Least Squares/Gradient Descent Methods for MIONets

    Jul 8, 2026Jun Choi, Chang-Ock Lee, Minam MoonGradient DescentNeural Network Optimization

    Department of Mathematical Sciences, KAIST, Daejeon 34141, KOREA · Department of Mathematics, Korea Military Academy, Seoul 01805, KOREA

  21. Kernel-based Operator Learning: Error Analysis, Budget Allocation, and a Physics-Informed Extension

    Jul 7, 2026Rüdiger KempfKernel MethodsPDE Operator Learning

    Applied and Numerical Analysis University of Bayreuth 95440 Bayreuth, Germany

  22. Quadrature-Aware Complex-Linear Neural Operator for Boundary-to-Field Prediction in Resonant Acoustics

    Jul 5, 2026Muhammad Idrees Khan, Hua-Dong YaoComplex-Valued Neural NetworksOperator Learning

    Department of Mechanical Engineering, Chalmers University of Technology Gothenburg 41296, Sweden

  23. Self-explainable Operator Learning for Discovering Spatial Patterns in Functional Data

    Jul 2, 2026Mojgan Alishiri, Amirhossein ArzaniNeural Network InterpretabilityOperator Learning

    1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, 84112, United States · 2Mechanical Engineering Department, University of Utah, Salt Lake City, UT, 84112, United States

  24. A Dual-domain Refinement Network with FBP-based Jacobian Learning for Sparse-view Dual-Energy CT Material Decomposition

    Jun 29, 2026Qian Liu, Xiaohong Fan, Ke Chen +3Sparse-View CT ReconstructionOperator Learning

    School of Mathematics and Computational Science, Xiangtan University, National Center for Applied Mathematics in Hunan, Key Laboratory for Intelligent Computing and Information Processing of the Ministry of Education, Xiangtan 411105, China · College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China · Department of Mathematics and Statistics, University of Strathclyde, G1 1XH Glasgow, U.K. +1

  25. A Trainable-by-Parts Operator Learning Framework: Bridging DeepONet and Karhunen-Loeve Expansions for Large-Scale Applications

    Jun 26, 2026Christian Munoz, Alexandre TartakovskyPDE Surrogate ModelingOperator Learning

    Civil Engineering Department University of Illinois Urbana-Champaign Urbana, IL 61801 · Pacific Northwest National Laboratory,Jun Richland, WA 99352

  26. DVL-DeepONet: A Physics-Guided Operator Learning for Resilient Underwater Navigation

    Jun 22, 2026Arup Kumar Sahoo, Itzik KleinUnderwater RoboticsPhysics-Informed ML

    The Hatter Department of Marine Technologies, Leon H. Charney School of Marine Sciences, University of Haifa, Haifa, 3498838, Israel

  27. Neural Operator Processes for Probabilistic Operator Learning under Partial Observations

    Jun 22, 2026Jose Miguel Lara-Rangel, Serge GuillasNeural ProcessesPDE Operator Learning

    University College London London, United Kingdom

  28. Generalization Guarantees for Multi-Input Neural Operator Learning in Sobolev Spaces

    Jun 16, 2026Yahong Yang, Zecheng Zhang, Wei Zhu +2Neural Network Approximation TheoryOperator Learning

    School of Mathematics, Georgia Institute of Technology, 686 Cherry Street, Atlanta, GA 30332-0160, USA. · Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, USA. · Department of Mathematics, Hong Kong Baptist University, FSC1202, Fong Shu Chuen Building, Hong Kong Baptist University, Kowloon Tong, Hong Kong.

  29. Deep Q-Learning on Hölder Spaces

    Jun 15, 2026Qian QiRL ControlDeep Q-Learning

    Peking University

  30. A Fixed-Point Neural Operator for Size- and Functional-Transferable Hamiltonian Prediction

    Jun 12, 2026Yunhong Lou, Xihang Yue, Xinran Wei +2Quantum ChemistryMolecular Property Prediction

    1Zhejiang University, Hangzhou, China. · 2Zhongguancun Academy, Beijing, China. · 3Zhongguancun Institute of Artificial Intelligence, Beijing, China.

  31. Deep Spectral Learning of Embedded Latent Transfer Operators for Stochastic Dynamical Systems

    Jun 12, 2026Ryogo Tanaka, Yoshinobu KawaharaDynamical SystemsStochastic Linear Dynamical Systems

    Graduate School of Information Science and Technology, The University of Osaka, Osaka, Japan · Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan

  32. Harness In-Context Operator Learning with Chain of Operators

    Jun 10, 2026Minghui Yang, Ling Guo, Liu YangOOD GeneralizationPDE Operator Learning

    Department of Mathematics, Shanghai Normal University, Shanghai, China · Department of Mathematics, National University of Singapore, Singapore

  33. Operator learning for the 2D incompressible Navier-Stokes equations: a conformal prediction approach in the data-scarce regime

    Jun 7, 2026Weinan Wang, Bowen Gang, Hao DengUncertainty QuantificationConformal Prediction

    Department of Mathematics, University of Oklahoma, Norman, OK, USA · Department of Statistics and Data Science, School of Management, Fudan University, Shanghai, China

  34. Spectral Audit of In-Context Operator Networks

    Jun 1, 2026Zhiwei Gao, Liu Yang, George Em KarniadakisPDE Operator LearningOperator Learning

    Division of Applied Mathematics, Brown University, Providence, RI, USA · Department of Mathematics, National University of Singapore, Singapore

  35. Variation Spaces for Encoder--Decoder Neural Operators: Approximation and Generalization

    May 31, 2026Jia-Qi Yang, Lei ShiNeural Network Approximation TheoryOperator Learning

    School of Mathematical Sciences and Shanghai Key Laboratory for Contemporary Applied Mathematics, Fudan University, Shanghai 200433, China.

  36. Is Zero-Shot Super-Resolution Possible in Operator Learning?

    May 29, 2026Unique Subedi, Ambuj TewariOperator LearningStatistical Learning Theory

    Department of Statistics, University of Michigan

  37. Functional Attention: From Pairwise Affinities to Functional Correspondences

    May 29, 2026Jiefang Xiao, Maolin Gao, Simon Weber +2PDE Operator LearningAttention Mechanisms

    Technical University of Munich, Germany · Munich Center for Machine Learning (MCML), Germany · PIXL, Department of Computer Science, University of Oxford, United Kingdom +1

  38. Measure-to-measure Regression with Transformers

    May 27, 2026Matthew Vandergrift, Martha White, Yury Polyanskiy +2TransformerOperator Learning

    University of Alberta · Canada CIFAR AI Chair · MIT +1

  39. High-Fidelity Industrial Crash Dynamics Prediction via Geometry-Aware Operator Learning with Memory-Efficient Low-Rank Attention

    May 26, 2026Deepak Akhare, Mohammad Amin Nabian, Corey Adams +2Low-Rank AttentionSurrogate Modeling

    Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN · NVIDIA, San Jose, CA · General Motors, Detroit, MI

  40. Operator Learning for Reconstructing Flow Fields from Sparse Measurements: a Language Model Approach

    May 22, 2026Qian Zhang, George Em KarniadakisPDE Operator LearningOperator Learning

    Division of Applied Mathematics, Brown University

  41. Optimization of randomized neural networks for transfer operator approximation

    May 22, 2026Mohammad Tabish, Stefan KlusDynamical SystemsNeural Network Optimization

    Maxwell Institute for Mathematical Sciences, University of Edinburgh and Heriot–Watt University, Edinburgh, UK · School of Mathematical & Computer Sciences, Heriot–Watt University, Edinburgh, UK

  42. Multiple Neural Operators Achieve Near-Optimal Rates for Multi-Task Learning

    May 21, 2026Adrien Weihs, Hayden SchaefferNeural Network Approximation TheoryOperator Learning

    Department of Mathematics, University of California Los Angeles, Los Angeles, CA 90095, USA.

  43. Nonlocal operator learning for fMRI encoding and decoding tasks

    May 19, 2026Andreas Kramer, Saugat Acharya, Alice Giola +1Neural DecodingNeuroimaging

    Department of Computer Science, Idaho State University 921 S. 8th Ave Mail Stop 8060, Pocatello, ID 83209-8023 · Department of Mathematics and Statistics, Idaho State University Physical Science Complex, 921 S. 8th Ave., Stop 8085, Pocatello, ID 83209

  44. Smooth Piecewise Cutting for Neural Operator to Handle Discontinuities and Sharp Transitions

    May 19, 2026Ha Dang, Sebastian Schmidt, Juergen HesserPDE Operator LearningDeep Operator Networks

    Mannheim Institute for Intelligent Systems in Medicine, Heidelberg University · Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University · Heidelberg Institute for Theoretical Studies (HITS), Heidelberg University +2

  45. Function graph transformers universally approximate operators between function spaces

    May 18, 2026Takashi Furuya, David Mis, Ivan Dokmanić +2Transformer AttentionNeural Network Approximation Theory

    Doshisha University, RIKEN AIP · Rice University · University of Basel +2

  46. Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations

    May 16, 2026Phuoc-Toan Huynh, Richard Archibald, Feng BaoUncertainty QuantificationDeep Operator Networks

    Department of Mathematics, Florida State University, Tallahassee, FL, USA · Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

  47. Universal Approximation of Nonlinear Operators and Their Derivatives

    May 14, 2026Filippo de FeoNeural Network Approximation TheoryOperator Learning

    Institut für Mathematik, Technische Universität Berlin.

  48. Eradicating Negative Transfer in Multi-Physics Foundation Models via Sparse Mixture-of-Experts Routing

    May 14, 2026Ellwil Sharma, Arastu SharmaSparse Mixture-of-ExpertsPDE Operator Learning

    Shodh AI

  49. UFO: A Domain-Unification-Free Operator Framework for Generalized Operator Learning

    May 12, 2026Hanli Qiao, George Em Karniadakis, Muhammad MuniruzzamanCross-Domain GeneralizationPDE Operator Learning

    Water and Mining Environment Unit, Geological Survey of Finland, Vuorimiehentie 5, Espoo, 02151, Finland · Division of Applied Mathematics, Brown University, 170 Hope Street, Providence, RI 02912, USA · Institute of Geosciences, University of Bonn, Kirschallee 1-3, Bonn, 53115, Germany

  50. Approximation of Maximally Monotone Operators : A Graph Convergence Perspective

    May 12, 2026Takashi Furuya, Yury Korolev, Takaharu YaguchiNeural Network Approximation TheoryOperator Learning

    Doshisha University, RIKEN AIP · Department of Mathematical Sciences, University of Bath, UK · Kyushu University, RIKEN AIP

  51. A Deep Risk Estimator for Known Operator Learning

    May 8, 2026Andreas Maier, Md Hasan, Paulina Conrad +1Neural Network GeneralizationNeural Network Approximation Theory

    Pattern Recognition Lab · Friedrich-Alexander-Universität Erlangen-Nürnberg · Erlangen, Germany

  52. Enabling Real-Time Training of a Wildfire-to-Smoke Map with Multilinear Operators

    May 5, 2026Zachary Morrow, Joseph Crockett, John D. Jakeman +1Surrogate ModelingWildfire Forecasting

    Sandia National Laboratories 1515 Eubank Blvd SE, Albuquerque, NM 87123

  53. HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs

    May 1, 2026Jinpai Zhao, Nishant Panda, Yen Ting Lin +3Neural Surrogate ModelingNeural Network Interpretability

    Oden Institute University of Texas at Austin Austin, TX, USA · Information Sciences, CAI-3 Los Alamos National Laboratory Los Alamos, NM 87545, USA · Norwegian University of Life Sciences Ås, Norway / Simula Research Lab. Oslo, Norway

  54. Physics-Guided Dimension Reduction for Simulation-Free Operator Learning of Stiff Differential-Algebraic Systems

    Apr 21, 2026Huy Hoang Le, Haoguang Wang, Christian Moya +2Reduced-Order ModelingDeep Operator Networks

    School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 USA · Department of Mathematics, Purdue University, West Lafayette, IN 47907 USA · Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102 USA

  55. Learning the Riccati solution operator for time-varying LQR via Deep Operator Networks

    Apr 20, 2026Jun Chen, Umberto Biccari, Junmin WangFeedback ControlDeep Operator Networks

  56. Neural Shape Operator Surrogates -- Expression Rate Bounds

    Apr 20, 2026Helmut Harbrecht, Christoph SchwabPDE Operator LearningNeural Network Approximation Theory

  57. Learning Affine-Equivariant Proximal Operators

    Apr 16, 2026Oriel Savir, Zhenghan Fang, Jeremias SulamEquivariant Neural NetworksOperator Learning

    Mathematical Institute for Data Science, Johns Hopkins University, Baltimore, MD 21218, USA

  58. A short tour of operator learning theory: Convergence rates, statistical limits, and open questions

    Feb 28, 2026Simone Brugiapaglia, Nicola Rares Franco, Nicholas H. NelsenOperator LearningStatistical Learning Theory

    Concordia University · Politecnico di Milano · Cornell University and The University of Texas at Austin

  59. Fluids You Can Trust: Property-Preserving Operator Learning for Incompressible Flows

    Feb 17, 2026Ramansh Sharma, Matthew Lowery, Houman Owhadi +1PDE Operator LearningOperator Learning

    Kahlert School of Computing University of Utah UT, USA · Department of Computing and Mathematical Sciences California Institute of Technology CA, USA

  60. Learned iterative networks: An operator learning perspective

    Dec 9, 2025Andreas Hauptmann, Ozan ÖktemImage ReconstructionComputational Imaging

    Research Unit of Mathematical Sciences, University of Oulu, Finland · Department of Computer Science, University College London, United Kingdom · Department of Mathematics, KTH - Royal Institute of Technology, Sweden