Neural Surrogate Modeling

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Overview

Neural surrogate modeling replaces repeated calls to expensive simulators with learned approximations of physical fields, system dynamics, or quantities of interest, making tasks such as design search, control, and Bayesian inference more practical. Common approaches include neural operators for PDE solution maps, graph neural networks for meshes and networks, reduced-order latent models, and physics-informed or hybrid models that combine learned components with governing equations. Researchers also use multi-fidelity training, active learning, and probabilistic models to reduce data needs and estimate uncertainty. A central challenge is that good one-step or in-distribution accuracy does not guarantee stable long rollouts, reliable extrapolation, physical consistency, or improved downstream decisions; classical solvers remain important when stringent accuracy and verification are required.

Latest papers

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418 papers45 papers in the last four weeks, down 15% on the four weeks before. 0.3% of all new papers.

  1. Towards Infinitely Long Neural Simulations: Self-Refining Neural Surrogate Models for Dynamical Systems

    Mar 18, 2026Qi Liu, Laure Zanna, Joan BrunaNeural Surrogate ModelingAutoregressive Modeling

    Courant Institute School of Mathematics, Computing, and Data Science, New York University, USA.

  2. Rapid Neural Network Prediction of Linear Block Copolymer Free Energies

    Mar 18, 2026Ian Chen, Alfredo Alexander-KatzFree Energy CalculationPolymer Property Prediction

    Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge, MA 02139

  3. A scalable neural bundle map for multiphysics prediction in lithium-ion battery across varying configurations

    Mar 17, 2026Zhiwei Zhao, Changqing Liu, Jie Lin +4Multiphysics SimulationNeural Surrogate Modeling

    College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China · School of Mechanical and Aerospace Engineering, Queen's University Belfast, Belfast BT9 5AH, UK

  4. Deep Learning-Driven Black-Box Doherty Power Amplifier with Pixelated Output Combiner and Extended Efficiency Range

    Mar 17, 2026Han Zhou, Haojie Chang, David WidenNeural Surrogate ModelingInverse Design

    Tampere University, 33720 Tampere, Finland · Chalmers University of Technology, 41296 Gothenburg, Sweden

  5. A Kolmogorov-Arnold Surrogate Model for Chemical Equilibria: Application to Solid Solutions

    Mar 16, 2026Leonardo Boledi, Dirk Bosbach, Jenna PoonoosamyNeural Surrogate ModelingKolmogorov-Arnold Networks

    Institute of Fusion Energy and Nuclear Waste Management (IFN-2), Forschungszentrum Jülich GmbH

  6. Windowed Fourier Propagator: A Frequency-Local Neural Operator for Wave Equations in Inhomogeneous Media

    Mar 15, 2026Yiyang Cai, Zixuan Qiu, Yunlu Shu +4PDE Operator LearningNeural Operators

    School of Mathematical Sciences, Fudan University, Shanghai, 200433, China · Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, 200438, China · Center for Applied Mathematics, Fudan University, Shanghai, 200438, China

  7. Surrogates for Physics-based and Data-driven Modelling of Parametric Systems: Review and New Perspectives

    Mar 13, 2026Matteo Giacomini, Pedro DíezSurrogate ModelingReduced-Order Modeling

    Laboratori de Càlcul Numeric (LaCàN), E.T.S. de Ingeniería de Caminos, Canales y Puertos, Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Barcelona, Spain. · Centre Internacional de Mètodes Numèrics en Enginyeria (CIMNE), Barcelona, Spain.

  8. Learning Pore-scale Multiphase Flow from 4D Velocimetry

    Mar 12, 2026Chunyang Wang, Linqi Zhu, Yuxuan Gu +9Porous Media FlowComputational Fluid Dynamics

    Department of Earth Science and Engineering, Imperial College London. · Department of Geology, Ghent University. · Department of Energy Science and Engineering, Stanford University. +2

  9. FloeNet: A mass-conserving global sea ice emulator that generalizes across climates

    Mar 12, 2026William Gregory, Mitchell Bushuk, James Duncan +8Climate ModelingClimate Emulation

    Atmospheric and Oceanic Sciences Program, Princeton University, NJ, USA · Geophysical Fluid Dynamics Laboratory, NOAA, Princeton, NJ, USA · Allen Institute for Artificial Intelligence (Ai2), Seattle, USA +1

  10. Deep Learning-Based Metamodeling of Nonlinear Stochastic Dynamic Systems under Parametric and Predictive Uncertainty

    Mar 12, 2026Haimiti Atila, Seymour M. J. SpenceNeural Surrogate ModelingUncertainty Quantification

    Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USA

  11. Inverse Neural Operator for ODE Parameter Optimization

    Mar 12, 2026Zhi-Song Liu, Wenqing Peng, Helmi Toropainen +5Parameter EstimationOrdinary Differential Equations

    Department of Computational Engineering, Lappeenranta-Lahti University of Technology, Lahti, Finland · Atmospheric Modelling Center Lahti (AMC-Lahti), Finland · University of Helsinki, Helsinki, Finland +3

  12. Differentiable Thermodynamic Phase-Equilibria for Machine Learning

    Mar 11, 2026Karim K. Ben Hicham, Moreno Ascani, Jan G. Rittig +1Scientific MLDifferentiable Programming

    RWTH Aachen University, Process Systems Engineering (AVT.SVT), 52074 Aachen, Germany · Forschungszentrum Jülich GmbH, Institute of Climate and Energy Systems ICE-1: Energy Systems Engineering, 52425 Jülich, Germany · JARA-ENERGY, 52056 Aachen, Germany

  13. Factorized Neural Implicit DMD for Parametric Dynamics

    Mar 11, 2026Siyuan Chen, Zhecheng Wang, Yixin Chen +4Dynamical SystemsNeural Surrogate Modeling

    Department of Computer Science, University of British Columbia, Vancouver, Canada · Department of Computer Science, University of Toronto, Toronto, Canada

  14. MCMC Informed Neural Emulators for Uncertainty Quantification in Dynamical Systems

    Mar 11, 2026Heikki Haario, Zhi-Song Liu, Martin Simon +1Ordinary Differential EquationsUncertainty Quantification

    Lappeenranta-Lahti University of Technology LUT, Lahti, Finland · Frankfurt University of Applied Sciences, Frankfurt am Main, Germany · University of Huddersfield, Huddersfield, United Kingdom

  15. A Learning-Based Superposition Operator for Non-Renewal Arrival Processes in Queueing Networks

    Mar 11, 2026Eliran SherzerQueueing TheoryTemporal Point Processes

    Industrial Engineering, Ariel University, Ramat HaGolan St 65, Ariel, 4070000, Israel.

  16. Surrogate models for nuclear fusion with parametric Shallow Recurrent Decoder Networks: applications to magnetohydrodynamics

    Mar 11, 2026M. Lo Verso, C. Introini, E. Cervi +3MagnetohydrodynamicsNeural Surrogate Modeling

    Department of Energy, Politecnico di Milano, Milano, 20133, Italy · MATHEP Group, Dept. of Energy "Galileo Ferraris", Politecnico di Torino, Torino, Italy · Autodesk Research, 6 Agar Street, London UK +1

  17. A neural operator for predicting vibration frequency response curves from limited data

    Mar 10, 2026D. Bluedorn, A. Badawy, B. E. Saunders +2Neural Surrogate ModelingNeural Operators

    Department of Mechanical and Aerospace Engineering, New Mexico State University, Las Cruces, NM 88003, USA. · Knight Foundation School of Computing and Information Science, Florida International University, Miami, FL 33199, USA. · Sandia National Laboratories, Albuquerque, NM 87123, USA.

  18. NN-OpInf: an operator inference approach using structure-preserving composable neural networks

    Mar 9, 2026Eric Parish, Anthony Gruber, Patrick Blonigan +1Structure-Preserving Neural NetworksReduced-Order Modeling

    Sandia National Laboratories, Livermore, CA, USA · Sandia National Laboratories, Albuquerque, NM, USA

  19. Tau-BNO: Brain Neural Operator for Tau Transport Model

    Mar 9, 2026Nuutti Barron, Heng Rao, Urmi Saha +6Neural Surrogate ModelingPDE Surrogate Modeling

    Department of Radiology and Biomedical Imaging, University of California San Francisco, USA · College of Computer Science and Engineering, Northeastern University, China · Department of Computer Science, Wake Forest University, USA +1

  20. Generative prediction of laser-induced rocket ignition with dynamic latent space representations

    Mar 8, 2026Tony Zahtila, Ettore Saetta, Murray Cutforth +3Neural Surrogate ModelingPDE Surrogate Modeling

    Center for Turbulence Research, Stanford University, USA · University of Naples Federico II, Italy

  21. A Dual-Graph Spatiotemporal GNN Surrogate for Nonlinear Response Prediction of Reinforced Concrete Beams under Four-Point Bending

    Mar 7, 2026Zhaoyang Ren, Qilin LiNeural Surrogate ModelingSpatiotemporal GNNs

    Discipline of Computing, School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Australia

  22. Physics-informed AI Accelerated Retention Analysis of Ferroelectric Vertical NAND: From Day-Scale TCAD to Second-Scale Surrogate Model

    Mar 6, 2026Gyujun Jeong, Sungwon Cho, Minji Shon +13Neural Surrogate ModelingPhysics-Informed Neural Operators

    School of Electrical and Computer Engineering, Georgia Institute of Technology, GA, USA · Semiconductor Research and Development, Samsung Electronics Co., Ltd, South Korea · NVIDIA, Santa Clara, CA, USA

  23. SurgFormer: Scalable Learning of Organ Deformation with Resection Support and Real-Time Inference

    Mar 6, 2026Ashkan Shahbazi, Elaheh Akbari, Kyvia Pereira +7Neural Surrogate ModelingDeformable Object Simulation

    Department of Computer Science, College of Connected Computing, Vanderbilt University · Vanderbilt Institute for Surgery and Engineering · Department of Biomedical Engineering, Vanderbilt University +1

  24. Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labels

    Mar 5, 2026Khai Nguyen, Petros Ellinas, Anvita Bhagavathula +1Learning to OptimizeNeural Surrogate Modeling

    Massachusetts Institute of Technology, Cambridge, MA, USA · Technical University of Denmark, Kongens Lyngby, Denmark

  25. Predicting Atomistic Transitions with Transformers

    Mar 5, 2026Henry Tischler, Wenting Li, Qi Tang +2Computational Materials ScienceNeural Surrogate Modeling

    Computing and Artificial Intelligence Division, Los Alamos National Laboratory, Los Alamos, NM 87545 · School of Engineering and Computer Science, University of Denver, Denver, CO 80210 · Department of Physics and Astronomy, University of Denver, Denver, CO 80210 +4

  26. Using the SEKF to Transfer NN Models of Dynamical Systems with Limited Data

    Mar 2, 2026Joshua E. Hammond, Tyler A. Soderstrom, Brian A. Korgel +1System IdentificationNeural Surrogate Modeling

    McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 East Dean Keeton St., Stop C0400, Austin, 78712, TX, United States · Energy Institute, The University of Texas at Austin, 2304 Whitis Ave., Stop C2400, Austin, 78712, TX, United States · Texas Materials Institute, The University of Texas at Austin, 204 E. Dean Keeton St., Stop C2201, Austin, 78712, TX, United States +2

  27. Machine Learning Models to Identify Promising Nested Antiresonance Nodeless Fiber Designs

    Mar 2, 2026Rania A. Eltaieb, Sophie LaRochelle, Leslie A. RuschSmall-Data LearningData-Efficient Learning

  28. Hyperparameter Trajectory Inference with Conditional Lagrangian Optimal Transport

    Mar 2, 2026Harry Amad, Mihaela van der SchaarNeural Surrogate ModelingOptimal Transport

    Department of Applied Mathematics and Theoretical Physics University of Cambridge, UK

  29. Neural Latent Arbitrary Lagrangian-Eulerian Grids for Fluid-Solid Interaction

    Feb 28, 2026Shilong Tao, Zhe Feng, Shaohan Chen +3Fluid-Structure InteractionPhysics-Based Simulation

    School of Computer Science, Peking University · Global Innovation Exchange, Tsinghua University · School of Electrical and Computer Science, University of Southampton

  30. Learning geometry-dependent lead-field operators for forward ECG modeling

    Feb 25, 2026Arsenii Dokuchaev, Francesca Bonizzoni, Stefano Pagani +2ElectrocardiographyNeural Surrogate Modeling

    Laboratory of Mathematics for Biology and Medicine, Università di Trento, Italy · MOX Laboratory, Department of Mathematics, Politecnico di Milano, Italy · Euler Institute, Università della Svizzera italiana, Switzerland