Surrogate Modeling

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Surrogate modeling studies how to approximate costly simulators or opaque predictors so they can be queried, optimized, or interpreted more efficiently. Common approaches include Gaussian processes and reduced-order models, neural operators and other deep networks, and hybrid or multi-fidelity methods that combine cheap simulations with a smaller number of accurate ones; active learning can select the most useful new evaluations. Applications range from PDE and climate emulation to engineering design, control, Bayesian optimization, and explanations of black-box models. A central challenge is balancing speed and average prediction accuracy with reliable uncertainty estimates, physical consistency, and performance on unseen regimes or decisions.

Latest papers

229 papers27 papers in the last four weeks, up 13% on the four weeks before. 0.2% of all new papers.

Probabilistic Retrofitting of Learned Simulators
Mar 2, 2026Cristiana Diaconu, Miles Cranmer, Richard E. Turner +2Surrogate Modeling PDE Surrogate Modeling
The Polymathic AI Collaboration · University of Cambridge · Alan Turing Institute
Polynomial Surrogate Training for Differentiable Ternary Logic Gate Networks
Feb 27, 2026Sai Sandeep Damera, Ryan Matheu, Aniruddh G. Puranic +1Differentiable Logic Gate Networks Differentiable Logic
University of Maryland, College Park, MD, USA
HURRI-GAN: A Novel Approach for Hurricane Bias-Correction Beyond Gauge Stations using Generative Adversarial Networks
Feb 27, 2026Noujoud Nadera, Hadi Majed, Stefanos Giaremis +4Spatiotemporal Forecasting Flood Forecasting
Center of Computation and Technology, Louisiana State University, Baton Rouge, 70803 LA, US · Saint-Joseph University of Beirut, Beirut, 1104, Lebanon · Department of Physics, Aristotle University of Thessaloniki, Thessaloniki , 54124, Greece +3
Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach
Feb 25, 2026Nathalie C. Pinheiro, Donghu Guo, Hannah P. Menke +4Surrogate Modeling PDE Surrogate Modeling
Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, London, SW7 2AZ UK · Institute of GeoEnergy Engineering, Heriot-Watt University, Edinburgh, EH14 1AS UK · Department of Civil and Environmental Engineering, Imperial College London, London, SW7 2AZ UK +2
DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models
Feb 25, 2026Jinpeng Li, Zhongyi Pei, Huaze Xue +3Multivariate Time Series Forecasting Time Series Forecasting
School of Software, BNRist, Tsinghua University · Tencent
Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates
Feb 24, 2026Nick Polson, Vadim SokolovSurrogate Modeling Gaussian Process Regression
Booth School of Business, University of Chicago. · Department of Systems Engineering and Operations Research, George Mason University.
MAST: A Multi-fidelity Augmented Surrogate model via Spatial Trust-weighting
Feb 24, 2026Ahmed Mohamed Eisa Nasr, Ali Elham, Haris Moazam SheikhSurrogate Modeling Multifidelity Modeling
Department of Aeronautics and Astronautics, University of Southampton, SO17 1BJ, Southampton, United Kingdom
WeirNet: A Large-Scale 3D CFD Benchmark for Geometric Surrogate Modeling of Piano Key Weirs
Feb 24, 2026Lisa Lüddecke, Michael Hohmann, Sebastian Eilermann +4Surrogate Modeling Computational Fluid Dynamics
Hydraulic Engineering Section, Civil Engineering, Helmut-Schmidt-University, Hamburg, Germany · Institute for Artificial Intelligence, Helmut-Schmidt-University, Hamburg, Germany · Professorship of Computer Science in Mechanical Engineering, Helmut-Schmidt-University, Hamburg, Germany +2
Shape-informed cardiac mechanics surrogates in data-scarce regimes via geometric encoding and generative augmentation
Feb 23, 2026Davide Carrara, Marc Hirschvogel, Francesca Bonizzoni +3Surrogate Modeling PDE Surrogate Modeling
MOX Laboratory, Department of Mathematics, Politecnico di Milano, Milano, Italy · Division 2.2 Process Simulation, Bundesanstalt für Materialforschung und -prüfung (BAM), Berlin, Germany · Department of Mathematics, University of Trento, Trento, Italy
Interpolation-Driven Machine Learning Approaches for Plume Shine Dose Estimation: A Comparison of XGBoost, Random Forest, and TabNet
Feb 23, 2026Biswajit Sadhu, Kalpak Gupte, Trijit Sadhu +1Surrogate Modeling Random Forest
Health Safety & Environment Group, Bhabha Atomic Research Centre, Mumbai, 400085, India. · Homi Bhabha National Institute, Mumbai, 400094, India. · Department of Computer Engineering & Technology, Dr. Vishwanath Karad MIT World Peace University, Pune, 411038, India. +1
Position: A Dynamical Systems Perspective is Needed to Advance Time Series Modeling
Feb 18, 2026Daniel Durstewitz, Christoph Jürgen Hemmer, Florian Hess +2Time Series Modeling Time Series Forecasting
Dept. of Theoretical Neuroscience, Central Institute of Mental Health, Mannheim, Germany · Faculty of Physics & Astronomy, Heidelberg Univ., Heidelberg, Germany · Interdisciplinary Center for Scientific Computing, Heidelberg, Germany
Stabilizing Test-Time Adaptation of High-Dimensional Simulation Surrogates via D-Optimal Statistics
Feb 17, 2026Anna Zimmel, Paul Setinek, Gianluca Galletti +2Test-Time Adaptation OOD Generalization
ELLIS Unit, LIT AI Lab, Institute for Machine Learning, JKU Linz, Austria · Emmi AI GmbH, Linz, Austria
Adaptive Uncertainty-Guided Surrogates for Efficient phase field Modeling of Dendritic Solidification
Feb 17, 2026Eider Garate-Perez, Kerman López de Calle-Etxabe, Oihana Garcia +3Surrogate Modeling Computational Materials Science
Intelligent Information Systems, Tekniker, Iñaki Goenaga 5, Eibar (20600) Spain · Faculty of Informatics UPV-EHU, Paseo Manuel de Lardizabal 1, Donostia (20700) Spain · Department of Computer Sciences and Artificial Intelligence, UPV-EHU, Donostia (20700) Spain +1
Deep Jump Gaussian Processes for Surrogate Modeling of High-Dimensional Piecewise Continuous Functions
Oct 24, 2025Yang Xu, Chiwoo ParkDeep Gaussian Processes Surrogate Modeling
Industrial & Systems Engineering, University of Washington, Seattle, WA, USA
Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM
Oct 9, 2025Oskar Bohn Lassen, Serio Angelo Maria Agriesti, Filipe Rodrigues +2Neural Surrogate Modeling Surrogate Modeling
Technical University of Denmark Kongens Lyngby, Denmark
Surrogate Graph Partitioning for Spatial Prediction
Oct 9, 2025Yuta Shikuri, Hironori FujisawaGraph Partitioning Surrogate Modeling
The Graduate University for Advanced Studies · Tokio Marine Holdings, Inc. · Institute of Statistical Mathematics +1
Bifidelity Parameter Estimation Using Conditional Diffusion Models
Apr 2, 2025Caroline Tatsuoka, Minglei Yang, Dongbin Xiu +1Simulation-Based Inference Surrogate Modeling
Department of Mathematics, The Ohio State University, Columbus, OH 43210, USA · Fusion Energy Science Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA · Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
Active Learning with Bayesian Multi-Fidelity Laplace Neural Operators for Oscillatory Parametric PDEs
Feb 1, 2025Bongseok Kim, Haoyang Zheng, Michael Penwarden +1Dynamical Systems Surrogate Modeling
School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, 47907, IN, USA. · Department of Mathematics, Purdue University, 150 North University Street, West Lafayette, 47907, IN, USA.
Surrogate Modeling for Explainable Predictive Time Series Corrections
Dec 27, 2024Alfredo Lopez, Florian SobieczkySurrogate Modeling Explainable Time Series Forecasting
Software Competence Center Hagenberg, Hagenberg, Austria.

Mar 2, 2026J/K move · Enter open · S save

Probabilistic Retrofitting of Learned Simulators

Cristiana Diaconu, Miles Cranmer, Richard E. Turner, Tanya Marwah+1

The Polymathic AI Collaboration · University of Cambridge · Alan Turing Institute

Surrogate Modeling PDE Surrogate Modeling

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Dominant approaches for modelling Partial Differential Equations (PDEs) rely on deterministic predictions, yet many physical systems of interest are inherently chaotic and uncertain. While training probabilistic models from scratch is possible, it is computationally expensive and fails to leverage the significant resources already invested in high-performing deterministic backbones. In this work, we adopt a training-efficient strategy to transform pre-trained deterministic models into probabilistic ones via retrofitting with a proper scoring rule: the Continuous Ranked Probability Score (CRPS). Crucially, this approach is architecture-agnostic: it applies the same adaptation mechanism across distinct model backbones with minimal code modifications. The method proves highly effective across different scales of pre-training: for models trained on single dynamical systems, we achieve 20-54% reductions in rollout CRPS and up to 30% improvements in variance-normalised RMSE (VRMSE) relative to compute-matched deterministic fine-tuning. We further validate our approach on a PDE foundation model, trained on multiple systems and retrofitted on the dataset of interest, to show that our probabilistic adaptation yields an improvement of up to 40% in CRPS and up to 15% in VRMSE compared to deterministic fine-tuning. Validated across diverse architectures and dynamics, our results show that probabilistic PDE modelling need not require retraining from scratch, but can be unlocked from existing deterministic backbones with modest additional training cost.