Diffusion Models in Simulation-Based Inference: A Tutorial Review

• URL: http://arxiv.org/abs/2512.20685v1
• Date: Mon, 22 Dec 2025 15:10:35 GMT
• Title: Diffusion Models in Simulation-Based Inference: A Tutorial Review
• Authors: Jonas Arruda, Niels Bracher, Ullrich Köthe, Jan Hasenauer, Stefan T. Radev,
• Abstract summary: Diffusion models have emerged as powerful learners for simulation-based inference ( SBI)<n>In this tutorial review, we synthesize recent developments on diffusion models for SBI.<n>We highlight opportunities created by various concepts such as guidance, score composition, flow matching, consistency models, and joint modeling.
• Score: 9.572470603492077
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models have recently emerged as powerful learners for simulation-based inference (SBI), enabling fast and accurate estimation of latent parameters from simulated and real data. Their score-based formulation offers a flexible way to learn conditional or joint distributions over parameters and observations, thereby providing a versatile solution to various modeling problems. In this tutorial review, we synthesize recent developments on diffusion models for SBI, covering design choices for training, inference, and evaluation. We highlight opportunities created by various concepts such as guidance, score composition, flow matching, consistency models, and joint modeling. Furthermore, we discuss how efficiency and statistical accuracy are affected by noise schedules, parameterizations, and samplers. Finally, we illustrate these concepts with case studies across parameter dimensionalities, simulation budgets, and model types, and outline open questions for future research.

Related papers

• Quantifying and Attributing Submodel Uncertainty in Stochastic Simulation Models and Digital Twins [0.1234398109349733]
  This paper investigates how submodel uncertainty affects the estimation of system performance metrics.<n>We develop a framework for quantifying submodel uncertainty in simulation models and extend the framework to digital-twin settings.
  arXiv  Detail & Related papers  (2026-02-18T00:06:39Z)
• Model-free Methods for Event History Analysis and Efficient Adjustment (PhD Thesis) [55.2480439325792]
  This thesis is a series of independent contributions to statistics unified by a model-free perspective.<n>The first chapter elaborates on how a model-free perspective can be used to formulate flexible methods that leverage prediction techniques from machine learning.<n>The second chapter studies the concept of local independence, which describes whether the evolution of one process is directly influenced by another.
  arXiv  Detail & Related papers  (2025-02-11T19:24:09Z)
• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• Provable Statistical Rates for Consistency Diffusion Models [87.28777947976573]
  Despite the state-of-the-art performance, diffusion models are known for their slow sample generation due to the extensive number of steps involved. This paper contributes towards the first statistical theory for consistency models, formulating their training as a distribution discrepancy minimization problem.
  arXiv  Detail & Related papers  (2024-06-23T20:34:18Z)
• All-in-one simulation-based inference [19.41881319338419]
  We present a new amortized inference method -- the Simformer -- which overcomes current limitations. The Simformer outperforms current state-of-the-art amortized inference approaches on benchmark tasks. It can be applied to models with function-valued parameters, it can handle inference scenarios with missing or unstructured data, and it can sample arbitrary conditionals of the joint distribution of parameters and data.
  arXiv  Detail & Related papers  (2024-04-15T10:12:33Z)
• Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
  Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
  arXiv  Detail & Related papers  (2024-04-11T09:23:36Z)
• A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks [0.0]
  We employ Bayesian neural networks to capture uncertainties in a reacting flow model. We demonstrate that BNN models can provide unique insights about the structure of uncertainty of the data-driven closure models. The efficacy of the model is demonstrated by a priori evaluation on a dataset consisting of a variety of flame conditions and fuels.
  arXiv  Detail & Related papers  (2024-02-28T22:19:55Z)
• Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models [26.178192913986344]
  We make a first attempt to use denoising diffusion probabilistic models (DDPMs) to train an uncertainty-aware surrogate model for turbulence simulations. Our results show DDPMs can successfully capture the whole distribution of solutions and, as a consequence, accurately estimate the uncertainty of the simulations. We also evaluate an emerging generative modeling variant, flow matching, in comparison to regular diffusion models.
  arXiv  Detail & Related papers  (2023-12-08T19:04:17Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled Trajectories [61.3299263929289]
  Simulation-based inference enables learning the parameters of a model even when its likelihood cannot be computed in practice. One class of methods uses data simulated with different parameters to infer an amortized estimator for the likelihood-to-evidence ratio. We show that this approach can be formulated in terms of mutual information between model parameters and simulated data.
  arXiv  Detail & Related papers  (2021-06-03T12:59:16Z)
• Amortized Bayesian model comparison with evidential deep learning [0.12314765641075436]
  We propose a novel method for performing Bayesian model comparison using specialized deep learning architectures. Our method is purely simulation-based and circumvents the step of explicitly fitting all alternative models under consideration to each observed dataset. We show that our method achieves excellent results in terms of accuracy, calibration, and efficiency across the examples considered in this work.
  arXiv  Detail & Related papers  (2020-04-22T15:15:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.