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.