Neural Surrogate Modeling

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39 papers in the last four weeks, up 388% on the four weeks before. 0.4% of all new papers.

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Machine Learning Surrogate Modeling for Homogenization of Hyperelastic Materials with Boolean Microstructures
May 31, 2026Matthias Brändel, Oliver RheinbachNeural Surrogate Modeling Hyperelasticity
Technische Universität Bergakademie Freiberg, Institut für Numerische Mathematik und Optimierung, Fakultät für Mathematik und Informatik, 09596 Freiberg, Germany.
An Exploratory Study into using Machine-Learning for Fast Step-by-step Emulation of Numerical Mechanical Thrombectomy Simulations for Ischemic Stroke
May 30, 2026Thijs StessenNeural Surrogate Modeling PDE Surrogate Modeling
University of Amsterdam · MSc Artificial Intelligence
Behavior Cloning of MPC for 3-DOF Robotic Manipulators
May 29, 2026Theo Guegan, Dexter Wen Jie TeoRobotic Control Neural Surrogate Modeling
Universite de Technologie de Compiégne, France · Opalin, Redwood City, CA · Nanyang Technological University, Singapore +1
GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization
May 29, 2026Zaid Khan, Justin Chih-Yao Chen, Jaemin Cho +2Neural Surrogate Modeling GPU Kernel Optimization
UNC Chapel Hill · AI2 · Johns Hopkins University +1
From Short Histories to Long Futures: Horizon-Aware Graph Neural Networks for Long Horizon Forecasting
May 28, 2026Zesheng Liu, Maryam RahnemoonfarNeural Surrogate Modeling Long-Term Time Series Forecasting
Department of Computer Science and Engineering, Lehigh University, Bethlehem PA 18015, USA · Department of Civil and Environmental Engineering, Lehigh University, Bethlehem2026 PA 18015, USA
MōLe-Λ: Learning the Coupled-Cluster Response State for Energies, Gradients, and Properties
May 28, 2026Andreas Burger, Luca Thiede, Abdulrahman Aldossary +4Neural Surrogate Modeling Quantum Chemistry
University of Toronto, Canada · Vector Institute for Artificial Intelligence, Toronto, Canada · NVIDIA, Toronto, Canada +2
Dark Quest II: A Wide-Coverage Neural Network Emulator of the Nonlinear Matter Power Spectrum Across Extended Cosmologies
May 27, 2026Satoshi Tanaka, Takahiro Nishimichi, Yosuke KobayashiNeural Surrogate Modeling Neural Network Generalization
Graduate School of Social Data Science, Hitotsubashi University, 2-1, Naka, Kunitachi 186-8601, Tokyo, Japan · Center for Gravitational Physics and Quantum Information, Yukawa Institute for Theoretical Physics, Kyoto University, Kyoto 606-8502, Japan · Department of Astrophysics and Atmospheric Sciences, Faculty of Science, Kyoto Sangyo University, Motoyama, Kamigamo, Kita-ku, Kyoto 603-8555, Japan +3
Resolution-free neural surrogates for geometric parameterization and mapping with spatially varying fields
May 27, 2026Yanwen Huang, Lok Ming Lui, Gary P. T. ChoiNeural Surrogate Modeling PDE Surrogate Modeling
Department of Mathematics, The Chinese University of Hong Kong
LEIA: Learned Environment for Interactive Architected Materials
May 27, 2026Haiqian Yang, Yuan Cao, Markus J. BuehlerNeural Surrogate Modeling World Model Learning
Unreasonable Labs · Mountain View, CA 94043, USA
Hybrid Neural World Models
May 27, 2026Pranav Lakshmanan, Paras ChopraNeural Surrogate Modeling Surrogate Modeling
Lossfunk
NUCLEUS-MoE: Unified Model of Pool Boiling for Liquid Cooling
May 26, 2026Arthur Feeney, Xianwei Zou, Sheikh Md Shakeel Hassan +2Neural Surrogate Modeling PDE Surrogate Modeling
Department of Electrical Engineering and Computer Science University of California, Irvine Irvine, CA, USA
Pretrained Approximators for Low-Thrust Trajectory Cost and Reachability
May 26, 2026Zhong Zhang, Giacomo Acciarini, Dario Izzo +2Neural Surrogate Modeling Trajectory Optimization
Politecnico di Milano · European Space Agency · Tsinghua University
Recursive Flow Matching
May 26, 2026Jiahe Huang, Sihan Xu, Sharvaree Vadgama +1Neural Surrogate Modeling Flow Matching
University of California, San Diego · University of Michigan
Semigroup Consistency as a Diagnostic for Learned Physics Simulators
May 25, 2026Lennon J. ShikhmanNeural Surrogate Modeling PDE Operator Learning
Georgia Institute of Technology
Accelerating Bayesian inverse design in computational fluid dynamics using neural operators
May 25, 2026Bipin Tiwari, Omer SanNeural Surrogate Modeling Bayesian Inverse Problems
Department of Mechanical and Aerospace Engineering, University of Tennessee, Knoxville,2026 TN 37996, USA
Samudra 2: Scaling Ocean Emulators across Resolutions
May 24, 2026Yuan Yuan, Jesse Rusak, Alexander Merose +5Neural Surrogate Modeling Climate Science
Courant Institute School of Mathematics, Computing, and Data Science, New York University, New York, NY, USA · Open Athena AI Foundation, Inc., New York, NY, USA · Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA
Courant: a State-Adaptive Perceiver-Based Neural Surrogate with Local Support and Interpretable Field Decomposition
May 24, 2026Anuj Kumar, Josiah Bjorgaard, Nikolaos Bouklas +2Neural Surrogate Modeling PDE Surrogate Modeling
Pasteur Labs, NY, NY, USA · Cornell University, Ithaca, NY, USA · Institute for Simulation Intelligence, NY, NY, USA
A comparative study of accuracy and rollout stability of temporal surrogate models
May 24, 2026Rajarshi BiswasNeural Surrogate Modeling Long-Term Time Series Forecasting
Cargill Inc. Wayzata, MN 55391, USA
High-fidelity Modeling of Full-scale Pressurized Water Reactor Flow Fields for Machine Learning Applications
May 23, 2026Logan A. Burnett, Hyungjun Kim, Hsien-Cheng Chou +5Neural Surrogate Modeling Spatiotemporal Forecasting
Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States · Korea Atomic Energy Research Institute, Daejeon, 34057, South Korea · Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, United States +2
Multi-Fidelity Learning with Shallow Recurrent Decoders for Reactor Physics
May 22, 2026Stefano Riva, Carolina Introini, J. Nathan Kutz +1Neural Surrogate Modeling Recurrent Neural Networks
Autodesk Research, London, UK · Department of Energy, Nuclear Engineering Division, Politecnico di Milano, Milan, Italy · Department of Mechanical and Nuclear Engineering and Emirates Nuclear Technology Center, Khalifa University, Abu Dhabi (127788), United Arab Emirates
LLM-driven design of physics-constrained constitutive models: two agents are better than one
May 22, 2026Marius Tacke, Matthias Busch, Kian Abdolazizi +4Neural Surrogate Modeling Multi-Agent LLM Systems
1Helmholtz-Zentrum Hereon, Geesthacht, Germany · 2Hamburg University of Technology, Hamburg, Germany · 3RWTH Aachen University, Aachen, Germany +2
Accelerating Divisible Load Processing Through Machine Learning: A Practical Framework for Large-Scale Workloads
May 22, 2026Bharadwaj VeeravalliNeural Surrogate Modeling
Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore
Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids
May 22, 2026Massimiliano Lupo Pasini, Yijiang Li, Kibaek Kim +1Neural Surrogate Modeling Heterogeneous GNNs
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37831, USA
Deep Learning Surrogates for Emulating Stochastic Climate Tipping Dynamics
May 20, 2026Adeline Hillier, Jennifer Sleeman, Jay Brett +3Neural Surrogate Modeling Multivariate Time Series Forecasting
Johns Hopkins Applied Physics Laboratory Hopkins University · Johns
Neural Operators for Design-Space Surrogate Modeling of Tendon-Actuated Continuum Robots
May 18, 2026Branden Frieden, James M. Ferguson, Alan Kuntz +1Neural Surrogate Modeling Fourier Neural Operator
The Robotics Center and the Kahlert School of Computing at the University of Utah, Salt Lake City, UT 84112, USA. · The Departments of Computer Science and Electrical and Computer Engineering at Vanderbilt University, Nashville, TN, 37203, USA.
The impact of observation density on Bayesian inversion of latent dynamics in shock-dominated flows
May 18, 2026Bipin Tiwari, Muhammad Abid, Omer SanNeural Surrogate Modeling Uncertainty Quantification
Department of Mechanical and Aerospace Engineering, University of Tennessee, Knoxville Knoxville, TN 37996, USA.
Geometry-Aware Surrogate for Real-Time Hydrodynamics Estimation of Autonomous Ground Vehicles in Amphibious Environments
May 18, 2026Ammar Waheed, Luke Gallantree, Zohaib HasnainNeural Surrogate Modeling PDE Surrogate Modeling
J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University, College Station, TX 77801, USA · Mounted Systems Engineering Team, Platform Systems Division, Defence Science and Technology Laboratory, Portsdown West, PO17 6AD, UK
Buffer-Parameterized Machine Learning Surrogate Models for Cross-Technology Signal Integrity Analysis and Optimization
May 18, 2026Julian Withöft, Werner John, Emre Ecik +2Neural Surrogate Modeling Surrogate-Assisted Optimization
Information Processing Lab, Faculty for Electrical Engineering and Information Technology, TU Dortmund, Germany · Pyramide2525, Paderborn, Germany · EMC Technology Center Paderborn, Zuken GmbH, Paderborn, Germany
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
Prediction Is Not Physics: Learning and Evaluating Conserved Quantities in Neural Simulators
May 16, 2026Andrew Bukowski, Aditya Kothari, Simba Shi +1Neural Surrogate Modeling Dynamical Systems
Yale University

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Machine Learning Surrogate Modeling for Homogenization of Hyperelastic Materials with Boolean Microstructures

Matthias Brändel, Oliver Rheinbach

Technische Universität Bergakademie Freiberg, Institut für Numerische Mathematik und Optimierung, Fakultät für Mathematik und Informatik, 09596 Freiberg, Germany.

Neural Surrogate Modeling Hyperelasticity Materials Property Prediction Materials Science Computational Materials Science

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Data-driven surrogate models are an alternative to numerical homogenization of heterogeneous materials. In this contribution, a supervised learning approach is presented for predicting effective Lamé parameters of hyperelastic composites from low-dimensional microstructural descriptors. The data set is based on previously published numerical homogenization results for ensembles of two-phase stochastic microstructures generated by planar Boolean models, covering variations of inclusion shape, phase contrast, and area fraction; see Brändel, Brands, Maike, Rheinbach, Schröder, Schwarz and Stoyan (2022). A neural network is trained on combinations of scalar and curve-valued statistical descriptors, including the area fraction, a derived scalar shape descriptor

τ

, the two-point correlation function

S_2(r)

, and the lineal-path function

\ell(z)

. Additional data representing limiting cases of the parameter space are incorporated to stabilize training and improve extrapolation behavior. The surrogate is evaluated by leave-one-grain-type-out cross-validation in order to assess generalization to unseen grain geometries. Numerical results demonstrate that additional descriptors can reduce relative errors. A predictor trained with

τ

and

S_2(r)

provides a compact representation with good quantitative accuracy and regular dense response behavior. Adding the lineal-path function

\ell(z)

further reduces the error at the available data points, indicating that it is a promising additional descriptor; however, dense post-training response evaluations show that improved pointwise accuracy does not automatically guarantee physically admissible behavior between sampled parameter values. This motivates future work on physically constrained surrogate models, loss formulations, bounded output parametrizations, and a more systematic representation of curve-valued geometric descriptors.

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