Upcoming events

07 January 2025 16:00 till 17:00

[AN] Tobias Werner: The Dirichlet Laplacian on a wedge

In this talk, I will present a new result for the Dirichlet Laplacian in wedges and polygons. Motivated by applications to stochastic partial differential equations, we consider appropriately weighted Sobolev spaces designed to capture singular behavior that are capable of incorporating singular behavior of the solutions, both at the boundary, as well as the singularities induced by the boundary itself. The key novelty is that the Dirichlet-Laplacian admits a bounded holomorphic functional calculus. This entails new maximal regularity results, in particular for the corresponding SPDE. The talk is based on joint work with Petru Cioica-Licht and Emiel Lorist.

13 January 2025 15:45 till 16:45

[STAT/AP] Rianne de Heide: Recent developments in e-value-based multiple testing

16 January 2025 16:00 till 17:00

[PDE&A] Jason Frank

17 January 2025 12:30 till 13:15

[NA] Richard Löscher: TBA

TBA

20 January 2025 15:45 till 16:45

[STAT/AP] Julia Olkhovskaya: Challenges and recent progress in reinforcement learning for large-scale environments.

Reinforcement learning (RL) has gained considerable attention recently as a framework for making sequential decisions in uncertain environments. While most of the existing research focuses on settings with a finite number of state-action pairs (the tabular setting) or simple models, like linear state-action value functions, it remains challenging to develop an RL algorithm that can efficiently manage large state-action spaces with more complex value functions. Recent studies have looked into nonlinear function approximations, with kernel ridge regression offering a natural step between linear models and neural networks. I will discuss the range of model complexities - from tabular representations to linear and nonlinear function approximations - and highlight some ongoing results and open problems in achieving statistical efficiency in these areas.

21 January 2025 16:00 till 17:00

[AN] Daniel Boutros

28 January 2025 16:00 till 17:00

[AN] Esmee Theewis

TBA

30 January 2025 16:00 till 17:00

[PDE&A] Jana Weber

11 February 2025 16:00 till 17:00

[AN] Mark van den Bosch: TBA

TBA

13 February 2025 16:00 till 17:00

[PDE&A] Rishabh Gvalani

14 February 2025 12:30 till 13:15

[NA] Amanda Howard: Multifidelity, domain decomposition, and stacking for improving training for physics-informed networks

Physics-informed neural networks and operator networks have shown promise for effectively solving equations modeling physical systems. However, these networks can be difficult or impossible to train accurately for some systems of equations. One way to improve training is through the use of a small amount of data, however, such data is expensive to produce. We will introduce our novel multifidelity framework for stacking physics-informed neural networks and operator networks that facilitates training by progressively reducing the errors in our predictions for when no data is available. In stacking networks, we successively build a chain of networks, where the output at one step can act as a low-fidelity input for training the next step, gradually increasing the expressivity of the learned model. We will finally discuss the extension to domain decomposition using the finite basis method, including applications to newly-developed Kolmogorov-Arnold Networks.

04 March 2025 16:00 till 17:00

[AN] Gabriele Brüll: TBA

TBA

11 March 2025 16:00 till 17:00

[AN] Yuming Zhao

13 March 2025 16:00 till 17:00

[PDE&A] Thomas Voigtmann

25 March 2025 16:00 till 17:00

[AN] Cyril Houdayer

10 April 2025 16:00 till 17:00

[PDE&A] Anh Khoa Doan: Scientific Machine Learning techniques for fluid mechanics

Machine learning techniques have in recent years seen an explosion in their development and application for fluid mechanics research problems. This is driven, in part, by the ability of machine learning approaches to model complex dynamics, allowing for improved predictions. Additionally, the embedding of physical information into deep learning architecture, to ensure the physicality of the predictions of the machine learning tools, is an on-going topic of research and has spurned architectures that can be used to solve inverse problems, and specifically augment the information content of a given dataset by reconstructing missing physical information.
In this talk, we will present machine learning applications along those two directions. First, we will present how a differentiable framework combined with deep learning can be used to complement an incomplete physical description of system. This will be illustrated on the case of a reacting flow, an archetypal multi-physics system that combines fluid mechanics and chemistry, the latter being the physics considered unknown. Second, we will present the use of physics-informed neural networks to augment experimental diagnostic. We will show how these can be used to reconstruct unmeasured quantities in a puffing pool fire and improve the spatial resolution of Particle Tracking Velocimetry measurements in a pulsed jet

16 May 2025 12:30 till 13:15

[NA] Martin Campos-Pinto: TBA

TBA