Artificial Intelligence and the Uncertainty challenge in Fundamental Physics

Session

Simulation Based Inference

28 nov. 2023, 11:30

Présidents de session

Simulation Based Inference: Amphi Durand, batiment Esclangon

• Louis Lyons

Simulation Based Inference: Amphi Astier, bâtiment Esclangon

• Gordon Watts (University of Washington)
• Wouter Verkerke (Nikhef/UvA)

53. An introduction to simulation-based inference

Gilles Louppe (University of Liège)

28/11/2023 11:30

In this talk, we will introduce simulation-based inference and present how deep learning can be used to solve complex inverse problems commonly found in scientific disciplines. We will give an introduction and overview of the topic and present some of our recent work on the topic. We will also discuss the opportunities and challenges.

35. Simulation Based Inference: The Frequentist Perspective Abstract:

Harrison Prosper (Florida State University)

28/11/2023 14:00

I give a brief introduction to the frequentist approach to simulation-based inference, which is often referred to as likelihood-free frequentist inference. The approach is illustrated with three simple examples, one from cosmology, one from particle physics, and the third from epidemiology.

31. Simulation-Based Inference: Where Classical Statistics Meets Machine Learning

Mikael Kuusela (Carnegie Mellon University)

28/11/2023 14:45

Simulation-based inference (SBI) refers to situations where the likelihood function cannot be readily evaluated but a simulator is available to generate data from a parametric model for any value of the unknown parameter. In recent years, a wide range of machine learning-based techniques have been developed to enable classical statistical inference in the simulation-based setting. These...

10. Swyft: Direct marginal inference for large simulation models

Christoph Weniger (University of Amsterdam)

28/11/2023 16:00

Simulation-Based Inference

As cosmology and astrophysics data advance, there is a growing demand for more detailed physical and instrumental simulation models with a multitude of uncertain parameters. Estimating the full joined posterior often becomes computationally prohibitive. Swyft is a deep learning python module that leverages the unique property of simulation-based inference to perform direct marginal inference....

6. Precision-Machine Learning for the Matrix Element Method

Nathan Huetsch (Heidelberg University)

28/11/2023 16:30

Simulation-Based Inference

The matrix element method is the LHC inference method of choice for limited statistics.
We present a dedicated machine learning framework, based on efficient phase-space
integration, a learned acceptance and transfer function. It is based on a choice of INN
and diffusion networks, and a transformer to solve jet combinatorics. Bayesian networks allow us to capture network uncertainties,...

14. Optimal dataset-wide inference in the presence of systematic uncertainties

Philipp Windischhofer (University of Chicago)

28/11/2023 17:00

Simulation-Based Inference

Real-world datasets often comprise sets of observations that collectively constrain the parameters of an underlying model of interest. Such models typically have a hierarchical structure, where "local" parameters impact individual observations and "global" parameters influence the entire dataset. In this talk we introduce Bayesian and Frequentist approaches for optimal dataset-wide...

34. SBI

Mikael Kuusela (Carnegie Mellon University)

36. SBI Introduction

Prof. Gilles Louppe (University of Liège)