What question did this study set out to answer?

The goal is to explore connections between high-dimensional control problems and machine learning techniques.

March 21, 2026Open Access

Mini-Workshop: High-Dimensional Control Problems and Mean-Field Equations with Applications in Machine Learning

Key Points

The goal is to explore connections between high-dimensional control problems and machine learning techniques.
Hosted a mini-workshop focusing on high-dimensional control issues and mean field equations.
Discussed numerical tools addressing the curse of dimensionality in optimization tasks.
Examined applications of control theory concepts within learning problems like deep neural networks.
Identified potential strategies for integrating control theoretical methods into machine learning.
Showed increased interest and development in numerical approaches to high-dimensional optimization problems.

Abstract

High-dimensional control problems and mean field equations have been of increased interest in recent years and novel numerical tools tackling the curse of dimensionality have been developed. These optimization tasks are strongly related to learning problems such as data-driven optimal control and learning of deep neural networks. As a consequence, there is a huge potential to employ control theoretical techniques in Machine Learning. The Mini-Workshop was devoted to discuss possible synergies among the various tools developed in these fields.

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Mini-Workshop: High-Dimensional Control Problems and Mean-Field Equations with Applications in Machine Learning

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Abstract

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