Surrogate Modeling

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21 papers in the last four weeks, up 200% on the four weeks before. 0.2% of all new papers.

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Latest papers 139

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Machine Learning-Driven Chemical Reactor Network Modeling of the Sandia-D Flame
Jun 1, 2026Nicolas J. Tricard, Benjamin C. Koenig, Sili DengSurrogate Modeling Computational Fluid Dynamics
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Scalable Derivative Gaussian Processes via Exact Gradient Reduction
Jun 1, 2026Hyunseok Seung, Matthias KatzfussSurrogate Modeling Gaussian Processes
Department of Statistics · University of Wisconsin–Madison · Madison, WI, 53706
TN-SHAP-G: Graph-Structured Tensor Network Surrogates for Shapley Values and Interactions
Jun 1, 2026Farzaneh Heidari, Guillaume RabusseauTensor Networks Surrogate Modeling
Universit´e de Montr´eal, Montr´eal, Quebec, Canada · Mila, Quebec AI Institute, Montreal, Quebec, Canada · CIFAR AI Chair.
Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA
May 30, 2026Xiao Jin, Yongxiong Wang, Haobo Liu +2Surrogate Modeling Surrogate-Assisted Optimization
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, 200093, Shanghai, China · Xinxiang University, 453003, Xinxiang, China · College of System Engineering, National University of Defense Technology, 410073, Changsha, China
Extending Causal Metamodeling to a non-Markovian Queue
May 30, 2026Pracheta Amaranath, Anant Bhide, David Jensen +1Surrogate Modeling Bayesian Networks
Manning College of Information and Computer Sciences, University of Massachusetts Amherst, MA, USA
Physically Constrained Ensemble Gaussian Process Modelling for Expensive Quantum Systems with Heteroskedastic Noise
May 29, 2026Arpan Biswas, Sutirtha Paul, Joseph Agada +2Surrogate Modeling Uncertainty Quantification
University of Tennessee-Oak Ridge Innovation Institute, University of Tennessee, Knoxville, TN 37996, USA · Institute for Advanced Materials and Manufacturing, University of Tennessee, Knoxville, Tennessee 37996, USA · Department of Physics and Astronomy, University of Tennessee, Knoxville, TN 37996, USA. +2
The Dynamic-Probabilistic Consistency Gap in Chaotic Surrogate Modeling
May 29, 2026Andre Herz, Matthijs Pals, Daniel Durstewitz +1Dynamical Systems Surrogate Modeling
1Interdisciplinary Center for Scientific Computing, Heidelberg University, Germany · Faculty of Mathematics and Computer Science, Heidelberg University, Germany · 3Dept. of Theoretical Neuroscience, Central Institute of Mental Health (CIMH), Mannheim, Germany +3
Hybrid Neural World Models
May 27, 2026Pranav Lakshmanan, Paras ChopraNeural Surrogate Modeling Surrogate Modeling
Lossfunk
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 Attention Surrogate Modeling
Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN · NVIDIA, San Jose, CA · General Motors, Detroit, MI
Accelerating Reinforcement Learning Training Using Simulation Surrogate Models
May 26, 2026Mohammadmahdi Ghasemloo, David J. Eckman, Yaxian LiSurrogate Modeling Reinforcement Learning
Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA · Intuit AI, Mountain View, CA, USA
FLOATBench: A Dataset and Benchmark for Floating Offshore Wind Turbine Tower Fatigue
May 25, 2026João Alves Ribeiro, Bruno Alves Ribeiro, Francisco Pimenta +2Surrogate Modeling Benchmark Construction
Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge, MA 02139 USA · CONSTRUCT, Faculty of Engineering University of Porto Porto, 4200-465 Portugal · Center for Mechanical Technology and Automation University of Aveiro Aveiro, 3810-193 Portugal +2
Explainable Wastewater Digital Twins: Adaptive Context-Conditioned Structured Simulators with Self-Falsifying Decision Support
May 19, 2026Gary Simethy, Daniel Ortiz Arroyo, Petar DurdevicSurrogate Modeling Safety-Critical Control
Department of Energy, Aalborg University, Niels Bohrs Vej 8, Esbjerg, 6700, Denmark
A Family of Divergence Measures for Evaluating the Reconstruction Quality of Explainable Ensemble Trees
May 19, 2026Massimo Aria, Agostino Gnasso, Carmela IorioSurrogate Modeling Ensemble Learning
Department of Economics and Statistics, University of Naples2026 Federico II, Naples, Italy
Emulating the Forced Response of Climate Models with Generative Machine Learning
May 16, 2026Graham Clyne, Julia Kaltenborn, Peer Nowack +2Neural Surrogate Modeling Surrogate Modeling
1INRIA, Paris, France · 2MILA, Montreal, Canada · 3Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Multi-Fidelity Flow Matching: Cascaded Refinement of PDE Solutions
May 15, 2026Sipeng Chen, Junliang Liu, Hewei Tang +1Surrogate Modeling Conditional Flow Matching
Department of Computer Science, Florida State University · Department of Materials Science and Engineering, Florida State University · Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin
CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models
May 15, 2026Stefano Riva, Carolina Introini, Antonio Cammi +13Surrogate Modeling Scientific ML
Autodesk Research, London, UK · Department of Energy, Nuclear Engineering Division, Politecnico di Milano, Milan, Italy · Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 +10
Watch your neighbors: Training statistically accurate chaotic systems with local phase space information
May 14, 2026Joon-Hyuk Ko, Andrus Giraldo, Deok-Sun LeeSurrogate Modeling Nonlinear System Identification
Center for AI and Natural Sciences Korea Institute for Advanced Study Seoul, Korea 02455 · School of Computational Sciences Korea Institute for Advanced Study Seoul, Korea 02455
StampFormer: A Physics-Guided Material-Geometry-Coupled Multimodal Model for Rapid Prediction of Physical Fields in Sheet Metal Stamping
May 13, 2026Jiajie Luo, Mohamed Mohamed, Osama Hassan +8Neural Surrogate Modeling Surrogate Modeling
Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not
May 12, 2026Kumbha Nagaswetha, Rabi PathakSurrogate Modeling Bayesian Optimization
Discovery of Interpretable Surrogates via Agentic AI: Application to Gravitational Waves
May 11, 2026Tousif Islam, Digvijay Wadekar, Tejaswi Venumadhav +4Surrogate Modeling Gravitational-Wave Astronomy
Kavli Institute for Theoretical Physics, University of California Santa Barbara, Kohn Hall, Lagoon Rd, Santa Barbara, CA 93106, USA. · Center for Gravitational Physics, University of Texas at Austin, Austin, TX 78712, USA. · Department of Physics, University of California at Santa Barbara, Santa Barbara, CA 93106, USA. +5
Support-Proximity Augmented Diffusion Estimation for Offline Black-Box Optimization
May 11, 2026Yonghan Yang, Ye Yuan, Zipeng Sun +5Surrogate Modeling Surrogate-Assisted Optimization
MBZUAI - Mohamed bin Zayed University of Artificial Intelligence · McGill University · Mila - Quebec AI Institute +1
Multifidelity Gaussian process regression for solving nonlinear partial differential equations
May 11, 2026Fatima-Zahrae El-Boukkouri, Josselin Garnier, Olivier RoustantSurrogate Modeling PDE Solving
Institut de Mathématiques de Toulouse, Université de Toulouse, INSA, Toulouse, 31077, France · CMAP, CNRS, Ecole polytechnique, Institut Polytechnique de Paris, Palaiseau, 91120, France
Physics-based Digital Twins for Integrated Thermal Energy Systems Using Active Learning
May 7, 2026Umme Mahbuba Nabila, Paul Seurin, Linyu Lin +1Surrogate Modeling Sparse Identification of Nonlinear Dynamics
Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI 48109, United States · Nuclear Science & Technology Division, Idaho National Laboratory, Idaho Falls, ID 83415, United States · Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, United States
Elicitation Matters: How Prompts and Query Protocols Shape LLM Surrogates under Sparse Observations
May 6, 2026Ge Lei, Samuel J. CooperSurrogate Modeling Bayesian Optimization
Dyson School of Design Engineering Imperial College London Exhibition Road, London SW7 2AZ
Enabling Real-Time Training of a Wildfire-to-Smoke Map with Multilinear Operators
May 5, 2026Zachary Morrow, Joseph Crockett, John D. Jakeman +1Surrogate Modeling Wildfire Forecasting
Sandia National Laboratories 1515 Eubank Blvd SE, Albuquerque, NM 87123
Multi-Fidelity Emulation of Atmospheric Correction Coefficients with Physics-Guided Kolmogorov-Arnold Networks
May 4, 2026Md Abdullah Al Mazid, Naphtali RisheSurrogate Modeling Remote Sensing
Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA
Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networks
May 4, 2026Haizhou Wen, Elham Kiyani, Gang Li +3Surrogate Modeling Surrogate-Assisted Optimization
Department of Mechanical Engineering, Clemson University, Clemson, SC 29634, USA · Division of Applied Mathematics, Brown University, Providence, RI 02912, USA · Center for Composite Materials, University of Delaware, Newark, DL 19716, USA +4
Generalizable Friction Coefficient Estimation via Material Embedding and Proxy Interaction Modeling
Apr 27, 2026Zhendong Wang, Huamin WangSurrogate Modeling Materials Property Prediction
Style3D Research Hangzhou, China
WISE-FM:Operation-Aware, Engineering-Informed Foundation Model for Multi-Task Well Design
Apr 26, 2026Carine de Menezes Rebello, Anderson Rapello dos Santos, Idelfonso B. R. NogueiraSurrogate Modeling Multi-Task Learning
Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway · Petróleo Brasileiro S.A. (PETROBRAS), Wells/Well Engineering, Rio de Janeiro, Brazil
Predicting Wind Loads on Container Ships in Harbor Environments through Multi-Fidelity Modeling
Apr 24, 2026Matilde Fiore, Andrea Bresciani, Miguel Alfonso Mendez +1Surrogate Modeling
Environmental and Applied Fluid Dynamics Department, von Karman Institute for Fluid Dynamics, Waterloosesteenweg 72, Sint-Genesius-Rode, B-1640, Belgium · Aero-Thermo-Mechanics Laboratory, École Polytechnique de Bruxelles, Université Libre de Bruxelles, Av. Franklin Roosevelt 50, Brussels, 1050, Belgium · Aerospace Engineering Research Group, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganés, 28911, Spain

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Machine Learning-Driven Chemical Reactor Network Modeling of the Sandia-D Flame

Nicolas J. Tricard, Benjamin C. Koenig, Sili Deng

Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

Surrogate Modeling Computational Fluid Dynamics

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Turbulent combustion simulations are crucial for many scientific and engineering systems. However, the high cost to fully resolve the complex multiscale and multiphysics behavior makes direct simulation typically infeasible. The equivalent reactor network (ERN) approach attempts to improve computational efficiency by replacing a multidimensional turbulent simulation with a series of much cheaper 0-D and 1-D chemical reactors, providing a surrogate model that retains detailed chemistry at the cost of simplified flow physics. However, their development remains a challenge, often requiring either expert analysis, or automated approaches that sacrifice accuracy. In this work, we develop an automated machine-learning-assisted framework for constructing ERNs of the Sandia-D turbulent methane/air flame. Principal component analysis is first used to reduce high-dimensional thermochemical computational fluid dynamics (CFD) data to a low-dimensional latent space, where k-means clustering identifies physically interpretable flame regions used to initialize a reactor-network graph. This initialization is then refined using finite-difference gradient descent wrapped around non-differentiable Cantera reactor simulations. Across 30 RANS simulations spanning a range of pilot temperatures and inlet methane compositions, the optimized 7-reactor ERN achieves a maximum-temperature

R^2

score of 0.7945 while preserving a

\sim6000\times

speedup over the CFD solver. Outlet CO prediction remains more challenging, with a final

R^2

score of

-0.4183

, but improves substantially from the unoptimized clustering initialization. These results show that unsupervised thermochemical feature extraction can provide effective physics-informed initializations for ERN construction, while gradient-based refinement can significantly improve predictive accuracy without manual reactor-network design.

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