Simulation-Based Inference

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Simulation-based inference uses a generative simulator to learn about unknown parameters when the likelihood of observed data is unavailable or too costly to compute. Methods include approximate Bayesian computation and neural posterior, likelihood, or ratio estimation, often using normalizing flows or diffusion models to produce posterior samples. These approaches are used in settings such as epidemiology, cosmology, engineering, and biological systems, and can be amortized to answer many inference queries after training. A central challenge is ensuring accurate, calibrated uncertainty when simulations are expensive or the simulator differs from reality; calibration tests, identifiability checks, and careful posterior validation remain essential.

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87 papers21 papers in the last four weeks, up 200% on the four weeks before. 0.1% of all new papers.

Keeping Score: Adaptive, Tuning-Free Loss Weighting for Score-Augmented Neural Ratio Estimation
May 12, 2026Alexander Shen, Mikael KuuselaNeural Surrogate Modeling Simulation-Based Inference
Department of Statistics and Data Science Carnegie Mellon University Pittsburgh, PA 15213
Pre-trained Tabular Foundation Models as Versatile Summary Networks for Neural Posterior Estimation
May 8, 2026Elliot Pickens, Chiraag Gohel, Sidharth SatyaTabular Foundation Models Simulation-Based Inference
University of Pennsylvania · The George Washington University · Brown University
Information-Preserving Domain Transfer with Unlabeled Data in Misspecified Simulation-Based Inference
May 7, 2026Joon Jang, Eunho Jeong, Kyu Sung Choi +1Synthetic-to-Real Domain Adaptation Simulation-Based Inference
Department of Biomedical Sciences, Seoul National University, Seoul, Republic of Korea · Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea · Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea +3
Neural Posterior Estimation of Terrain Parameters from Radar Sounder Data
May 5, 2026Jordy Dal Corso, Annalena Kofler, Marco Cortellazzi +2Simulation-Based Inference Parameter Estimation
University of Trento, Trento, Italy · Max Planck Institute for Intelligent Systems, Tübingen, Germany · Max Planck Institute for Gravitational Physics (Albert Einstein Institute), Potsdam, Germany +1
Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health
Apr 22, 2026Peter Collett, Alexander Johannes Stasik, Simone Casolo +1Simulation-Based Inference Efficient Inference
Cognite AS, Oslo, Norway · Department of Data Science, Norwegian University of Life Sciences, Ås, Norway · Department for Mathematics and Cybernetics, SINTEF Digital, Oslo, Norway
Tokenised Flow Matching for Hierarchical Simulation Based Inference
Apr 22, 2026Giovanni Charles, Cosmo Santoni, Seth Flaxman +1Flow Matching Simulation-Based Inference
Imperial College London, London, United Kingdom · University of Oxford, Oxford, United Kingdom
Generative Flow Networks for Model Adaptation in Digital Twins of Natural Systems
Apr 22, 2026Pascal Archambault, Houari Sahraoui, Eugene SyrianiSimulation-Based Inference Model Calibration
DIRO, Université de Montréal, Quebec, Canada.
Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference
Apr 20, 2026Jonas Arruda, Sophie Chervet, Paula Staudt +6Simulation-Based Inference Amortized Inference
Bonn Center for Mathematical Life Sciences, University of Bonn, Bonn, Germany · Life & Medical Sciences Institute, University of Bonn, Bonn, Germany · Epidemiology and Modeling of Antibiotic Evasion Unit, Institut Pasteur, Paris, France +12
Gradient estimators for parameter inference in discrete stochastic kinetic models
Apr 2, 2026Ludwig Burger, Annalena Kofler, Lukas Heinrich +1Straight-Through Estimator Parameter Estimation
Physics of Complex Biosystems, School of Natural Sciences, James-Franck-Straße 1, 85748 Garching, Germany · Max Planck Institute for Intelligent Systems, Max-Planck-Ring 4, 72076 Tübingen, Germany · Max Planck Institute for Gravitational Physics (Albert Einstein Institute), Am Mühlenberg 1, 14476 Potsdam, Germany +2
Conditional Neural Bayes Ratio Estimation for Experimental Design Optimisation
Mar 27, 2026S. A. K. Leeney, T. Gessey-Jones, W. J. Handley +3Bayesian Experimental Design Simulation-Based Inference
Cavendish Astrophysics, University of Cambridge, Cambridge CB3 0HE, UK. · Kavli Institute for Cosmology, University of Cambridge, Cambridge CB3 0HA, UK. · PhysicsX, London, UK. +1
Identifiability and amortized inference limitations in Kuramoto models
Mar 23, 2026Emma Hannula, Jana de Wiljes, Matthew T. Moores +2Kuramoto Model Amortized Inference
LUT University, Yliopistonkatu 34, Lappeenranta, 53850, Finland · TU Ilmenau, Ehrenbergstraße 29, Ilmenau, 98693, Germany · University of Cambridge, Herschel Road, Cambridge, CB3 9AL, United Kingdom
CogFormer: Learn All Your Models Once
Mar 20, 2026Jerry M. Huang, Lukas Schumacher, Niek Stevenson +1Amortized Inference Simulation-Based Inference
Center for Modeling, Simulation, and Imaging in Medicine (CeMSIM), Rensselaer Polytechnic Institute, Troy, New York, United States · Center for Economic Psychology, University of Basel, Basel, Switzerland · Amsterdam Mathematical Psychology Lab, University of Amsterdam , Amsterdam, Netherlands
Improving moment tensor solutions under Earth structure uncertainty with simulation-based inference
Mar 19, 2026A. A. Saoulis, T. -S. Pham, A. M. G. FerreiraSeismology Uncertainty Quantification
Department of Physics & Astronomy, University College London, Gower Street, London, WC1E 6BT, United Kingdom · Department of Earth Sciences, University College London, 5 Gower Place, London, WC1E 6BS, United Kingdom · Research School of Earth Sciences, The Australian National University, Canberra, ACT, Australia
Scalable Simulation-Based Model Inference with Test-Time Complexity Control
Mar 16, 2026Manuel Gloeckler, J. P. Manzano-Patrón, Stamatios N. Sotiropoulos +2Model Selection Simulation-Based Inference
Machine Learning in Science, University of Tübingen and Tübingen AI Center, Tübingen, Germany · Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK · National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, United Kingdom +1
ForwardFlow: Simulation only statistical inference using deep learning
Mar 11, 2026Stefan BöhringerRobust Statistics Simulation-Based Inference
Department of Biomedical Data Sciences Leiden University Medical Center Einthovenweg 20, 2333 ZC Leiden, The Netherlands
First Estimation of Model Parameters for Neutrino-Induced Nucleon Knockout Using Simulation-Based Inference
Mar 10, 2026Karla Tame-Narvaez, Steven Gardiner, Aleksandra Ćiprijanović +1Parameter Estimation Model Calibration
Fermi National Accelerator Laboratory, Batavia, Illinois 60510 USA · Department of Astronomy and Astrophysics, University of Chicago, Chicago, IL 60637 · NSF-Simons AI Institute for the Sky (SkAI), 172 E. Chestnut St., Chicago, IL 60611
Towards Reliable Simulation-based Inference
Mar 9, 2026Arnaud DelaunoySimulation-Based Inference Probability Calibration
Department of Electrical Engineering and Computer Science, Faculty of Applied Sciences
A Fast Generative Framework for High-dimensional Posterior Sampling: Application to CMB Delensing
Mar 4, 2026Hadi Sotoudeh, Pablo Lemos, Laurence Perreault-LevasseurPosterior Sampling Simulation-Based Inference
Department of Physics, Université de Montréal, Montréal, Canada · Mila - Quebec AI Institute, Montréal, Canada · Ciela, Montreal Institute for Astrophysics and Machine Learning, Montréal, Canada +1
Do Diffusion Models Dream of Electric Planes? Discrete and Continuous Simulation-Based Inference for Aircraft Design
Feb 27, 2026Aurelien Ghiglino, Daniel Elenius, Anirban Roy +7Diffusion Models Conditional Diffusion Models
Computer Science Laboratory, SRI · Aerospace Design Laboratory, Stanford University
Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model
Feb 17, 2026M Lopes Alves, Joel Dyer, Doyne Farmer +2Agent-Based Modeling Simulation-Based Inference
Department of Computer Science Institute of New Economic Thinking University of Oxford Oxford, UK · Department of Computer Science University of Oxford Oxford, UK
OneFlowSBI: One Model, Many Queries for Simulation-Based Inference
Jan 30, 2026Mayank Nautiyal, Li Ju, Melker Ernfors +5Simulation-Based Inference Amortized Inference
Department of Information Technology, Uppsala University, Uppsala, Sweden · Science for Life Laboratory, Uppsala University, Uppsala, Sweden
Adapting Neural Posterior Estimators for Simulation-Based Inference in Agent-Based Models
Jan 12, 2026Junyao Zhang, Jinglai Li, Junqi TangSimulation-Based Inference Neural Posterior Estimation
School of Mathematics, University of Birmingham
Diffusion Models in Simulation-Based Inference: A Tutorial Review
Dec 22, 2025Jonas Arruda, Niels Bracher, Ullrich Köthe +2Simulation-Based Inference Parameter Estimation
Bonn Center for Mathematical Life Sciences, Life & Medical Sciences Institute, University of Bonn, Germany · Center for Modeling, Simulation, & Imaging in Medicine (CeMSIM), Rensselaer Polytechnic Institute, NY, USA · Computer Vision and Learning Lab, Heidelberg University, Germany
Posterior-driven Heuristic Support Adaptation in a Probabilistic Treatment of Real2Sim2Real for Vision-Driven Deformable Linear Object Manipulation
Oct 30, 2025Georgios Kamaras, Craig Innes, Subramanian RamamoorthySimulation-Based Inference Adaptive Inference
School of Informatics, The University of Edinburgh, EH8 9AB, UK. · Heriot-Watt University · UKRI Turing AI World Leading Researcher Fellowship on AI for Person-Centred and Teachable Autonomy
Estimating Orbital Parameters of Direct Imaging Exoplanet Using Neural Network
Oct 20, 2025Bo Liang, Hanlin Song, Chang Liu +9Markov Chain Monte Carlo Simulation-Based Inference
Center for Gravitational Wave Experiment, National Microgravity Laboratory, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China · Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou), University of Chinese Academy of Sciences (UCAS), Beijing 100049, China · National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China +6
Causal Posterior Estimation
May 27, 2025Simon Dirmeier, Antonietta MiraBayesian Inference Simulation-Based Inference
Swiss Data Science Center, ETH Zurich, Switzerland · Università della Svizzera italiana, Switzerland · Insubria University, Italy
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

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Keeping Score: Adaptive, Tuning-Free Loss Weighting for Score-Augmented Neural Ratio Estimation

Alexander Shen, Mikael Kuusela

Department of Statistics and Data Science Carnegie Mellon University Pittsburgh, PA 15213

Neural Surrogate Modeling Simulation-Based Inference

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Neural likelihood surrogates (e.g., Neural Ratio Estimation) for stochastic process models are commonly trained via probabilistic classification on simulated data, which forces a tradeoff between surrogate quality and training costs. For structured models where the exact score

\nabla_θ\log p(x \mid θ)

is available, this information can be incorporated into training by augmenting the cross-entropy loss with a score-matching term. However, the optimal weighting of the two losses is not known a priori, and selecting it by hand requires expensive tuning that undercuts the computational savings. We propose an adaptive, tuning-free algorithm that sets the score loss weights during training based on loss gradients, adding minimal overhead to standard classifier training. We evaluate our approach on case studies involving network dynamics and spatial processes, demonstrating that it improves surrogate quality at a drastically lower computational cost than generating more training data. Notably, in some cases, our approach achieves downstream inference performance equivalent to a 10x increase in training data with less than a 1.1x increase in training time.