Conveners
AI for Mathematics and Mathematics for AI / Foundation Inference Models for SDEs: Scientific Session
- Adrian Riekert
- Julian Kranz
- Benno Kuckuck
- Arnulf Jentzen
- Ramsés Sanchéz (Lamarr institute, University of Bonn)
Description
This joint session consists of the following blocks:
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AI for Mathematics and Mathematics for AI
Speakers: Benno Kuckuck, Julian Kranz, Adrian Riekert, Arnulf Jentzen from the University of Münster. -
Discussion.
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Foundation Inference Models for SDEs: Towards AI Agents that Build and Reason about Mathematical Models of Data.
Speaker: Ramses J. Sanchez
Abstract: AI agents augmented with LLMs are becoming commonplace. In the near future, LLM-based agents for mathematical modeling will require access to fast and accurate tools, to infer mathematical equations they can reason about directly from data. However, inferring equations from data — also known as the inverse problem or system identification problem — remains a fundamental and open challenge in machine learning. Current state-of-the-art solutions are slow, unstable, and heavily reliant on prior knowledge, making them inaccessible to scientists unfamiliar with ML — let alone AI agents(!). In this presentation, I will discuss Foundation Inference Models, a methodology we have developed over the past two years that enables zero-shot inference of equations directly from data. This time, I will focus on Stochastic Differential Equations (SDEs), which serve as flexible mathematical frameworks for modeling complex systems across disciplines, from statistical physics to finance and climate science. -
Discussion
