We run a series of seminars for researchers to showcase their work in using simulations to derive science results.

This interest group provides a knowledge exchange with a focus on the most recent advances in simulation-based inference and their applications in multiple disciplines. The main goal is to promote the exchange of ideas and create a supportive environment to work on similar data analysis challenges encountered in multiple research areas. In particularly, we are interested in:

• Developing methods that tightly couple simulations to observations
• Identifying new approaches for surrogate models of costly simulators
• Highlighting methods for improving model predictions through the application of experimentation and active learning
• Advancing the field of simulation through the combination of mechanistic models with deep learning
• Promote early-career researchers (through talks/colloquia)

Talking points:

• How can we retain the explainability of mechanistic models whilst improving their predictive power?
• How do we separate out epistemic and aleatoric errors in model evaluation?
• How do we most effectively deploy simulations to advance science and how do we identify simulations that can lead to incorrect conclusions?
• How do we improve trust in how simulations are used in society?
• How can we ensure simulations are robust to new evidence?