This website under construction.
Introduction
Simulators are the modern manifestation of scientific theories. They implement mechanistic models of the underlying natural phenomena of interest as well as models for the instruments used to observe those phenomena. The expressiveness of programming languages facilitates the development of complex, highfidelity simulations and the power of modern computing provides the ability to generate synthetic data from them. The flexibility of simulators has made them critical research tools (and major cyberinfrastructure investments) for predicting how systems will behave across many areas of science and engineering. Unfortunately, despite their predictive power, these simulators are poorly suited for statistical inference, which is a core aspect of dataintensive science. To meet this challenge, there are an emerging set of techniques for simulationbased inference (SBI).
Simulationbased inference is the next step in the methodological evolution of statistical practice in the sciences. SBI provides qualitatively new capabilities that can transform scientific practice in fields as diverse as evolutionary biology, systems biology, neuroscience, gravitational wave astronomy, dark matter astrophysics, cosmology, and particle physics. Inference problems in these areas are challenging because they involve highdimensional, richlystructured spaces. Empowering domain scientists with the ability to directly infer from data the properties of the underlying mechanistic models that they are developing would be transformative.
SBI has also proven to be an effective lingua franca that facilitates communication between domain scientists and methodological experts, supports convergence research, and accelerates crosspollination of ideas between fields.
Selected Papers
The plan is to turn this page into a crowdsourced community resource that can collect recent papers including methodological developments and applications. Here are some links to get started:
Reviews

The frontier of simulationbased inference review by Kyle Cranmer, Johann Brehmer, and Gilles Louppe

Google Scholar searches for “Simulationbased inference”, “likelihoodfree”, and “Approximate Bayesian Computation”
Applications

Particle Physics: Simulationbased inference methods for particle physics by Johann Brehmer and Kyle Cranmer in “Artificial Intelligence for Particle Physics”, World Scientific Publishing Co.

Computational Neuroscience: Training deep neural density estimators to identify mechanistic models of neural dynamics by Pedro J Gonçalves, JanMatthis Lueckmann, Michael Deistler, Marcel Nonnenmacher, Kaan Öcal, Giacomo Bassetto, Chaitanya Chintaluri, William F Podlaski, Sara A Haddad, Tim P Vogels, David S Greenberg, Jakob H Macke

Gravitational Wave Astronomy: RealTime Gravitational Wave Science with Neural Posterior Estimation by Maximilian Dax, Stephen R. Green, Jonathan Gair, Jakob H. Macke, Alessandra Buonanno, and Bernhard Schölkopf

Astroparticle Physics: Inferring dark matter substructure with astrometric lensing beyond the power spectrum by Siddharth MishraSharma

Astroparticle Physics: A neural simulationbased inference approach for characterizing the Galactic Center by Siddharth MishraSharma, Kyle Cranmer

Cosmology: SimulationBased Inference of Strong Gravitational Lensing Parameters by Ronan Legin, Yashar Hezaveh, Laurence Perreault Levasseur, Benjamin Wandelt

Cosmology: SimulationBased Inference of Reionization Parameters From 3D Tomographic 21 cm Lightcone Images by Zhao, Xiaosheng ; Mao, Yi ; Cheng, Cheng ; Wandelt, Benjamin D.
 Genomics: Addressing uncertainty in genomescale metabolic model reconstruction and analysis by David B. Bernstein, Snorre Sulheim, Eivind Almaas & Daniel Segrè in Genome Biology volume 22, Article number: 64 (2021)
Furthermore, genomescale metabolic models (GEMs) can be used to simulate disparate types of ‘omics data, even though the explicit calculation of likelihoods may be intractable. Thus, the use of “simulationbased” Bayesian inference approaches is a promising route for informing GEM structure and parameters from data [198]. However, scaling Bayesian approaches up to deal with the large space of possible GEM reconstructions is an open, exciting and challenging research direction.

Evolutionary Biology: Simulationbased inference of evolutionary parameters from adaptation dynamics using neural networks by Grace Avecilla, Julie N. Chuong, Fangfei Li, Gavin Sherlock, David Gresham, Yoav Ram

Evolutionary Biology: Universal probabilistic programming offers a powerful approach to statistical phylogenetics by Fredrik Ronquist, Jan Kudlicka, Viktor Senderov, Johannes Borgström, Nicolas Lartillot, Daniel Lundén, Lawrence Murray, Thomas B. Schön & David Broman

Global Health: SimulationBased Inference for Global Health Decisions by Christian Schroeder de Witt, Bradley GramHansen, Nantas Nardelli, Andrew Gambardella, Rob Zinkov, Puneet Dokania, N. Siddharth, Ana Belen EspinosaGonzalez, Ara Darzi, Philip Torr, Atılım Güneş Baydin
 Robotics: Simulationbased Bayesian inference for multifingered robotic grasping by Norman Marlier, Olivier Brüls, Gilles Louppe
Selected Software
An initial list of SBIrelated software packages
 SBI (python)  general purpose SBI framework
 SBI Benchmarking (python)  for benchmarking
 MadMiner  aimed at particle physics
 swyft (python) the reference implementation of Truncated Marginal Neural Ratio Estimation (TMNRE), an efficient, simulationbased inference technique for complex data and expensive simulators
 carl an early NNbased SBI package
About this site
This page is maintained by Kyle Cranmer and hosted via GitHub pages via the simulationbasedinference GitHub organization. As mentioned above, the plan is to turn this page into a crowdsourced community resource that can collect recent papers including methodological developments and applications. We are working on the underlying infrastructure, but it will probably be similar to what drives the IRISHEP webpages (source) and/or something like this living review.