Theoretical
Foundations
of Deep Learning

DFG-funded Priority Program 2298

Learn More
Logo of LMU Munich
23

Projects

34

Prinicipal
Investigators

24

Universities from across Germany

Theoretical Foundations of Deep Learning

Towards a better understanding of deep learning

Parallel to the impressive success of deep learning in real-world applications ranging from autonomous driving to gaming intelligence and healthcare, deep learning-based methods are now also making a strong impact in science, replacing or complementing state-of-the-art classical model-based methods in solving mathematical problems such as inverse problems or partial differential equations.

However, despite the outstanding successes, most of the research on deep neural networks is empirically driven and their theoretical-mathematical foundations are largely lacking. The main goal of this priority program is to develop a comprehensive theoretical foundation of deep learning.

Map of Germany with markers at the locations of all the projects

Three complementary viewpoints

The statistical perspective, which views neural network training as a statistical learning problem and investigates expressivity, learning, optimization, and generalization,

The application perspective, which focuses on security, robustness, interpretability, and fairness

The mathematical-methodological perspective, which develops and theoretically analyzes novel Deep Learning-based approaches to solving inverse problems and partial differential equations.

A woman writing equations on a white board

Many relevant fields

The research questions to be addressed in this priority program are to a large extent interdisciplinary in nature and can only be solved by a joint effort of mathematics and computer science.

Mathematical methods and concepts from all areas of mathematics are required, including algebraic geometry, analysis, stochastics, approximation theory, differential geometry, discrete mathematics, functional analysis, optimal control, optimization, and topology.

Statistics and theoretical computer science also play a fundamental role. In this sense, methods from mathematics, statistics and computer science form the core of this priority program.

A robot hand

News & Info

Public Lecture

May 26, 2023
Click here
DFG Slides
Call for ProposalsCoordinator Slides
DFG Slides
Application
Click here

Michael Möller, a principal investigator of the SPP, was invited to give a public talk at the University of Siegen. To find out more, please click the link down below.

Best Thesis Award

March 7, 2023
Click here
DFG Slides
Call for ProposalsCoordinator Slides
DFG Slides
Application
Click here

Applications for the FoDL Best Thesis Award are now open. For more information, please click the link down below.

GAMM Annual Meeting

June 2, 2023
Click here
DFG Slides
Call for ProposalsCoordinator Slides
DFG Slides
Application
Click here

For the second year in a row, the Priority Program is taking part in the annual meeting of the Society for Applied Mathematics and Mechanics (GAMM).

Events

PhD course

May 27, 2024

Outreach Lars Grüne

April 9, 2024

Annual Meeting 2023

November 8, 2023

Phase 2 Online Info Event

October 18, 2023

Workshop in Bayreuth

May 30, 2022

Virtual Kick-off Meeting

January 18, 2022

Junior Researcher Meetup

April 6, 2022

Annual Meeting 2022

November 20, 2022
See All Events

News from the AI community at LMU

Find the latest news and upcoming events from the various groups researching artificial intelligence and its applications at LMU Munich.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Logo of LMU Munich