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Wed, 05. Mar at 15:15
rooms 405/406, WI...
Nonlocal particle approximation for diffusion PDEs
Abstract
Mon, 10. Mar at 13:30
WIAS 405-406
First Optimize, Then Discretize for Scientific Machine Learning
Abstract. This talk provides an infinite-dimensional viewpoint on optimization problems encountered in scientific machine learning and discusses the paradigm first optimize, then discretize for their solution. This amounts to first choosing an appropriate infinite-dimensional algorithm which is subsequently discretized in the tangent space of the neural network ansatz. To illustrate this point, we show that recently proposed state-of-the-art algorithms for scientific machine learning applications can be derived within this framework. Finally, we discuss the crucial aspect of scalability of the resulting algorithms.
Wed, 12. Mar at 13:00
ZIB, Room 2006 (S...
Bounding geometric penalties in Riemannian optimization
Abstract. Riemannian optimization refers to the optimization of functions defined over Riemannian manifolds. Such problems arise when the constraints of Euclidean optimization problems can be viewed as Riemannian manifolds, such as the symmetric positive-definite cone, the sphere, or the set of orthogonal linear layers for a neural network. This Riemannian formulation enables us to leverage the geometric structure of such problems by viewing them as unconstrained problems on a manifold. The convergence rates of Riemannian optimization algorithms often rely on geometric quantities depending on the sectional curvature and the distance between iterates and an optimizer. Numerous previous works bound the latter only by assumption, resulting in incomplete analysis and unquantified rates. In this talk, I will discuss how to remove this limitation for multiple algorithms and as a result quantify their rates of convergence.
Wed, 19. Mar at 13:00
ZIB, Room 2006 (S...
Entanglement detection via Frank-Wolfe algorithms
Wed, 26. Mar at 13:00
ZIB, Room 2006 (S...
Advancing Climate Strategies - High-Resolution Canopy Height Estimation from Space
Abstract. Reliable and detailed information on forest canopy height is essential for understanding the health and carbon dynamics of forests, which play a pivotal role in climate adaptation and mitigation strategies. Traditional methods of forest monitoring, while foundational, lack the global coverage and are often costly, hindering effective policymaking. Jan Pauls and colleagues have developed a novel framework using satellite data to estimate canopy height on a global scale. The approach combines cutting-edge data preprocessing techniques, a unique loss function to mitigate geolocation inaccuracies, and data filtering from the Shuttle Radar Topography Mission to enhance prediction reliability in mountainous areas. The framework significantly improves upon existing global-scale canopy height maps. By offering a high-resolution (10 m) global canopy height map, the produced map provides critical insights into forest dynamics, aiding in more effective forest management and climate change mitigation efforts. This talk will explore the methods and implications of this work, demonstrating how advancements in Earth observation and machine learning can revolutionize global forest assessments and ecological studies.
Wed, 09. Apr at 13:00
ZIB, Room 2006 (S...
Koopman von Neumann mechanics
Wed, 16. Apr at 16:30
EN 058
D-Finite Functions
Abstract. A function is called D-finite if it satisfies a linear differential equations with polynomial coefficients. Such functions play a role in many different areas, including combinatorics, number theory, and mathematical physics. Computer algebra provides many algorithms for dealing with D-finite functions. Of particular importance are operations that preserve D-finiteness. In the talk, we will give an overview over some of these techniques.
Wed, 23. Apr at 11:30
online
Entanglement Detection via Frank-Wolfe Algorithms
Abstract
Tue, 29. Apr at 11:15
1.023 (BMS Room, ...
Wed, 07. May at 11:30
online
A New Approach to Metastability in Multi-Agent Systems
Abstract
Wed, 07. May at 15:15
WIAS, Erhard-Schm...
Tue, 13. May at 11:15
1.023 (BMS Room, ...
Wed, 14. May at 14:00
WIAS, Erhard-Schm...
Wed, 14. May at 15:30
WIAS, Erhard-Schm...
Wed, 21. May at 11:30
online
Demand Strip Packing
Wed, 04. Jun at 11:30
online
On the Expressivity of Neural Networks
Abstract
Wed, 18. Jun at 11:30
online
Convolutional Brenier Generative Networks
Abstract
Wed, 02. Jul at 11:30
online
Informing Opinion Dynamics Models with Online Social Network Data
Abstract
Wed, 16. Jul at 11:30
online
Data-Adaptive Discretization of Inverse Problems
Abstract