SFB 1481

Research topic

Our research center is concerned with the mathematical foundations for computational approaches in machine learning, signal processing and simulation. Despite vast gains in the speed of computers, the deluge of data and complexity of models describing natural and technical phenomena pose fundamental challenges that cannot be surmounted by computational power alone. Two critical fronts for which the time is ripe to make progress are 

  1. signal processing and machine learning with huge data sets and; 
  2. partial differential equations with singularities such as point defects or interfaces. 

Significantly expanding the frontier in these areas requires new insight into the underlying mathematical structure of the problems. While the two mentioned challenges may appear to have little in common, their analysis will benefit from closely related ideas and algorithms, in particular, from those based on sparsity, that is, low complexity structures in high dimensions.

​The SFB 1481 combines the expertise of several mathematicians at RWTH Aachen University working in analysis, probability theory, numerical analysis, optimization and algebra and encompasses 18 scientific sub-projects addressing challenging problems in the above mentioned areas.

News

  • ERC Consolidator Grant for Markus Bachmayr

    Markus Bachmayr, PI and Vice Speaker of SFB 1481, receives an ERC Consolidator Grant 2024. His project "Computational Complexity of Highly Nonlinear…

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  • Young Researcher Award 2024 for Matthieu Dolbeault

    Matthieu Dolbeault, independent postdoc at SFB 1481, receives the Young Researcher Award 2024 of Journal of Complexity for his work on…

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  • Upcoming Events

  • Martin Burger (University of Hamburg)

    SeMath
    Colloquium

    Title: Optimal Transport and Gradient Flows on Networks
    Abstract: In this talk we will discuss the mathematical modelling and analysis of optimal…

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  • Borjan Geshkovski (Inria Paris, Sorbonne Université)

    SeMath
    Colloquium

    Title: Some mathematical perspectives on Transformers
    Abstract: Since their introduction in 2017, Transformers have revolutionized large language…

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  • André Uschmajew (University of Augsburg)

    SeMath
    Colloquium

    TBA

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