Conference Agenda

Brief Description and Outline:

Simulation-Based Inference (SBI) is rapidly emerging as a powerful paradigm for scientific discovery, enabling parameter estimation, uncertainty quantification, and model selection in complex systems. However, significant challenges remain in translating SBI’s potential into widespread practical application. This workshop, “Open Challenges in Simulation-Based Inference,” will bring together domain scientists actively employing SBI and developers of core SBI methodologies to identify and address these critical bottlenecks. We aim to foster a collaborative environment for knowledge exchange, promoting mutual understanding of needs, benefits, and limitations across diverse scientific fields. While SBI methods have advanced rapidly, their application often faces hurdles related to computational cost, robustness to noise, scalability to high-dimensional parameter spaces, and effective integration into existing scientific workflows. Many domain scientists encounter specific challenges unique to their fields that are not adequately addressed by current methodological developments. This workshop directly addresses the need for a dedicated space to bridge this gap, facilitating a dialogue between end-users and method developers. Addressing these challenges is crucial for unlocking the full potential of SBI and accelerating scientific progress across disciplines. This workshop aligns with the HAICON’s focus on scientific interdisciplinary research using machine learning.

This workshop will employ a highly interactive format designed to maximize engagement and knowledge sharing:

• [60 min] Keynote Address: A leading expert will present a comprehensive overview of the current state of SBI methods, outlining recent advancements and highlighting open research questions. (We are actively pursuing [Keynote Speaker Name/Area of Expertise] as a potential speaker.)

• [30 min] Lightning Talks: Selected poster presenters will deliver concise (3-5 minute) lightning talks to introduce their challenges and research to the broader audience.

• [120 min] Poster Session & Challenge Forum: The core of the workshop, dedicated to in-depth discussion and exploration of submitted posters. We will utilize structured community interviews during the poster session to systematically identify common challenges and potential solutions.

• [30 min] Wrap-up & Summary: A facilitated discussion will synthesize key findings from the poster session and lightning talks, leading to a concise summary of identified challenges and potential research directions.

Goals:

Simulation-based inference (SBI) has become an essential tool across a wide and rapidly growing range of scientific domains — from biology and astronomy to geology and physics. Yet despite this breadth, practitioners across these fields frequently encounter the same fundamental challenges: questions about scalability, model misspecification, validation, and the gap between methodological advances and practical applicability. These challenges are rarely addressed in a cross-domain setting, leaving communities to rediscover solutions in isolation. This workshop aims to change that. By bringing together SBI method developers, domain scientists, and practitioners under one roof, we seek to create a dedicated space for structured exchange that is rarely possible within the boundaries of a single-domain conference. Our concrete objectives are to foster the adoption and continued development of SBI by connecting those who build these methods with those who apply them; to sharpen the community's collective understanding of the limitations of SBI and the mitigations available; and to provide an accessible, representative overview of the field that serves both newcomers and experienced practitioners.

Presenters Experience:

We hope to attract a seasoned sbi scientist to deliver an in-depth keynote to kick-off our workshop.

 

Target Audience:

SBI users from any scientific domain and SBI method developers

Keywords:

Inverse Problems, Generative AI, Simulation-Based Inference, Community, Machine Learning