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. Conditional Neural Field based Reduced Order Model for Dynamic Ditching Load Prediction

    May 5, 2026Henning Schwarz, Pyei Phyo Lin, Jens-Peter M. Zemke +1Neural Surrogate ModelingImplicit Neural Representations

    Institute for Fluid Dynamics and Ship Theory, Hamburg University of Technology, Am Schwarzenberg-Campus 4, D-21073 Hamburg, Germany · Institute of Mathematics, Hamburg University of Technology, Am Schwarzenberg-Campus 3, D-21073 Hamburg, Germany

  2. Graph Neural Networks in the Wilson Loop Representation of Abelian Lattice Gauge Theories

    May 5, 2026Ali Rayat, Gia-Wei ChernNeural Surrogate ModelingGraph Neural Networks

    Department of Physics, University of Virginia, Charlottesville, VA 22904, USA

  3. LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning

    May 4, 2026Hongwei Jin, Keunju Song, Zeeshan Memon +5Neural Surrogate ModelingBenchmark Design

    Mathematics and Computer Science Division, Argonne National Laboratory · Department of Electronic Engineering, Sogang University · Department of Computer Science, Emory University +1

  4. 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

  5. Teacher Forcing as Generalized Bayes: Optimization Geometry Mismatch in Switching Surrogates for Chaotic Dynamics

    Apr 28, 2026Andre Herz, Daniel Durstewitz, Georgia KoppeNeural Surrogate ModelingGeneralized Bayesian Inference

    Interdisciplinary Center for Scientific Computing, Heidelberg University, Germany · Dept. of Theoretical Neuroscience, Central Institute of Mental Health (CIMH), Mannheim, Germany · Hector Institute for AI in Psychiatry and Dept. of Psychiatry and Psychotherapy, CIMH, Mannheim, Germany +1

  6. Geometry-free prediction of inertial lift forces in microfluidic devices using deep learning

    Apr 27, 2026Jesse Ward-Bond, Ali Mashadian, Timothy C. Y. Chan +1Neural Surrogate ModelingOOD Generalization

    aDepartment of Mechanical & Industrial Engineering, University of Toronto · bMechanical Engineering, Purdue University · cDepartment of Materials Science & Engineering, University of Toronto +1

  7. Relaxation-Informed Training of Neural Network Surrogate Models

    Apr 24, 2026Calvin TsayNeural Surrogate ModelingMixed-Integer Linear Programming

    Department of Computing, Imperial College London, South Kensington, SW7 2AZ, United Kingdom.

  8. SOC-ICNN: From Polyhedral to Conic Geometry for Learning Convex Surrogate Functions

    Apr 24, 2026Kang Liu, Jianchen Hu, Wei PengNeural Surrogate ModelingSecond-Order Cone Programming

    School of Future Technology Xi’an Jiaotong University Xi’an, China · School of Future Technology School of Automation Science and Engineering Xi’an Jiaotong University Xi’an, China · School of Automation Science and Engineering Xi’an Jiaotong University Xi’an, China

  9. Neural surrogates for crystal growth dynamics with variable supersaturation: explicit vs. implicit conditioning

    Apr 23, 2026Matteo Rigoni, Daniele Lanzoni, Francesco Montalenti +1Neural Surrogate ModelingConvolutional RNNs

    Dept. of Materials Science, University of Milano-Bicocca, 20125 Milano, Italy · Dept. of Physics, University of Genova, 16146 Genova, Italy

  10. Assessing Emulator Design and Training for Modal Aerosol Microphysics Parameterizations in E3SMv2

    Apr 23, 2026Shady E. Ahmed, Hui Wan, Saad Qadeer +5Neural Surrogate ModelingScientific ML

    Advanced Computing, Mathematics, and Data Division Pacific Northwest National Laboratory Richland, WA 99354 · Earth and Biological Sciences Directorate Pacific Northwest National Laboratory Richland, WA 99354 · Equifax Inc., Atlanta, GA +2

  11. A Hybridizable Neural Time Integrator for Stable Autoregressive Forecasting

    Apr 22, 2026Brooks Kinch, Xiaozhe Hu, Yilong Huang +6Neural Surrogate ModelingTime Series Transformers

    Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, USA · Department of Mathematics, Tufts University, Medford, MA, USA · Department of Mathematics and Cybernetics, SINTEF Digital, Oslo, Norway +3

  12. Learning to Emulate Chaos: Adversarial Optimal Transport Regularization

    Apr 22, 2026Gabriel Melo, Leonardo Santiago, Peter Y. LuNeural Surrogate ModelingChaotic Dynamical Systems

    LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau · Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC · University of Campinas +1

  13. climt-paraformer: Stable Emulation of Convective Parameterization using a Temporal Memory-aware Transformer

    Apr 22, 2026Shuochen Wang, Nishant Yadav, Joy Merwin Monteiro +1Neural Surrogate ModelingClimate Science

    Sustainability and Data Sciences Laboratory, Northeastern University · Microsoft · Indian Institute of Science Education and Research Pune +2

  14. A Deep U-Net Framework for Flood Hazard Mapping Using Hydraulic Simulations of the Wupper Catchment

    Apr 22, 2026Christian Lammers, Fernando Arévalo, Leonie Märker-Neuhaus +3Neural Surrogate ModelingU-Net

    Institute for Computing and Information Sciences Radboud University Nijmegen, Netherlands · Wupper Association (Wupperverband) Wuppertal, Germany

  15. Data-Driven Open-Loop Simulation for Digital-Twin Operator Decision Support in Wastewater Treatment

    Apr 22, 2026Gary Simethy, Daniel Ortiz Arroyo, Petar DurdevicNeural Surrogate ModelingDigital Twins

    Aalborg University, Department of Energy, Niels Bohrs Vej 8, Esbjerg, DK-6700, Denmark

  16. Skillful Global Ocean Emulation and the Role of Correlation-Aware Loss

    Apr 20, 2026Niraj Agarwal, Timothy A. Smith, Sergey Frolov +1Neural Surrogate ModelingSpatiotemporal Forecasting

    CIRES, University of Colorado, Boulder, CO, USA · NOAA Physical Sciences Laboratory, Boulder, CO, USA

  17. Scalable Physics-Informed Neural Differential Equations and Data-Driven Algorithms for HVAC Systems

    Apr 20, 2026Hanfeng Zhai, Hongtao Qiao, Hassan Mansour +1Neural Surrogate ModelingPhysics-Informed ML

    Department of Mechanical Engineering, Stanford University, Stanford, CA · Mitsubishi Electric Research Laboratories, Cambridge, MA

  18. Neural Adjoint Method for Meta-optics: Accelerating Volumetric Inverse Design via Fourier Neural Operators

    Apr 19, 2026Chanik Kang, Hyewon Suk, Haejun ChungNeural Surrogate ModelingFourier Neural Operator

    Hanyang University, Department of Artificial Intelligence Semiconductor Engineering Seoul, Korea · Hanyang University, Department of Electronic Engineering Seoul, Korea

  19. FLARE: A Data-Efficient Surrogate for Predicting Displacement Fields in Directed Energy Deposition

    Apr 17, 2026Kittipong Thiamchaiboonthawee, Ghadi Nehme, Ram Mohan Telikicherla +5Neural Surrogate ModelingImplicit Neural Representations

    Massachusetts Institute of Technology, Cambridge, MA, USA · GE Vernova Advanced Research Center, Niskayuna, NY, USA

  20. Predicting Power-System Dynamic Trajectories with Foundation Models

    Apr 16, 2026Haoran Li, Lihao Mai, Chenhan Xiao +2Neural Surrogate ModelingZero-Shot Time Series Forecasting

    School of Electrical, Computer and Energy Engineering at Arizona State University, Tempe, AZ 85281 USA · MOVEJ Analytics, Dayton, OH 45324 USA

  21. SOLIS: Physics-Informed Learning of Interpretable Neural Surrogates for Nonlinear Systems

    Apr 16, 2026Murat Furkan Mansur, Tufan KumbasarNeural Surrogate ModelingParameter Estimation

    AI and Intelligent Systems Laboratory, Istanbul Technical University, 34469, Istanbul, Türkiye

  22. Physics-Informed Machine Learning for Pouch Cell Temperature Estimation

    Apr 16, 2026Zheng LiuNeural Surrogate ModelingPDE Surrogate Modeling

    Department of Industrial and Manufacturing Systems Engineering University of Michigan-Dearborn Dearborn, USA

  23. Material-Agnostic Zero-Shot Thermal Inference for Metal Additive Manufacturing via a Parametric PINN Framework

    Apr 16, 2026Hyeonsu Lee, Jihoon JeongNeural Surrogate ModelingZero-Shot Learning

    Wm Michael Barnes ’64 Department of Industrial and Systems Engineering Texas A&M University College Station, TX 77843, USA

  24. TRACER: Trace-Based Adaptive Cost-Efficient Routing for LLM Classification

    Apr 16, 2026Adam RidaNeural Surrogate ModelingLLM Inference Efficiency

    Independent Researcher.

  25. AutoSurrogate: An LLM-Driven Multi-Agent Framework for Autonomous Construction of Deep Learning Surrogate Models in Subsurface Flow

    Apr 13, 2026Jiale Liu, Nanzhe WangNeural Surrogate ModelingPDE Surrogate Modeling

    School of Physics and Astronomy, The University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom · Institute of GeoEnergy Engineering, School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom · Subsurface Energy Transition and Innovation Centre, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom

  26. Emulating Non-Differentiable Metrics via Knowledge-Guided Learning: Introducing the Minkowski Image Loss

    Apr 13, 2026Filippo Quarenghi, Ryan Cotsakis, Tom BeuclerNeural Surrogate Modeling

    Faculty of Geosciences and Environment, Expertise Center for Climate Extremes (ECCE), University of Lausanne, Lausanne, VD, Switzerland · Expertise Center for Climate Extremes (ECCE), University of Lausanne, Lausanne, VD, Switzerland

  27. Toward Generalizable Graph Learning for 3D Engineering AI: Explainable Workflows for CAE Mode Shape Classification and CFD Field Prediction

    Apr 9, 2026Tong Duy Son, Kohta Sugiura, Marc Brughmans +7Graph Representation LearningPhysics-Informed GNNs

    The authors are with Siemens Digital Industries Software, Leuven, Belgium.

  28. Physics-Informed Neural Optimal Control for Precision Immobilization Technique in Emergency Scenarios

    Apr 7, 2026Yangye Jiang, Jiachen Wang, Daofei LiOptimal ControlAutonomous Driving Planning

    Institute of Power Machinery and Vehicular Engineering, Zhejiang University and the Zhejiang Key Laboratory of Intelligent Vehicle Comprehensive Safety, Hangzhou 310027, China

  29. Predictions of charge density distributions for nuclei with Z≥8Z \geq 8

    Apr 7, 2026Yun Dong Wang, Tian Shuai Shang, Hui Hui Xie +3Neural Surrogate Modeling

    College of Physics, Jilin University, Changchun 130012, China · Department of Physics, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan · Quark Nuclear Science Institute, The University of Tokyo, Tokyo 113-0033, Japan +1

  30. Hybrid Fourier Neural Operator for Surrogate Modeling of Laser Processing with a Quantum-Circuit Mixer

    Apr 6, 2026Mateusz Papierz, Asel Sagingalieva, Alix Benoit +3Neural Surrogate ModelingFourier Neural Operator

    Terra Quantum AG, Kornhausstrasse 25, 9000 St. Gallen, Switzerland · Laboratory for Advanced Materials Processing, Feuerwerkerstrasse, Thun, 3603, BE, Switzerland