Mini-Workshop on Scientific Machine Learning and Uncertainty Quantification

to R149, RWTH main building (Templergraben 55)

The topics of scientific machine learning and of uncertainty quantification, which are closely related in their mathematical underpinnings, play an important role in CRC 1481, but are also an important research topic at the German Aerospace Center (DLR, Cologne). This workshop will provide an opportunity for interaction between the research groups.

The workshop takes place in room 149 (first floor of the RWTH main building), followed by a joint dinner at Mi Restaurant.

Preliminary program:

  • 14:30-15:15 - Philipp Knechtges (DLR): SciML @ DLR: Aerodynamics, Rocket Engines and more
  • 15:20-15:40 - Fabian Hoppe (DLR): Bringing Algorithms into Application
  • 15:40-16:00 - Fabrice von der Lehr (DLR): Bayesian Filtering for Blackbox Simulators
  • 16:00-16:30 - Coffee break
  • 16:30-16:50 - Raúl Tempone (RWTH, CRC 1481): Post-Hoc Uncertainty Quantification for Cardiac MRI: Robust Left Ventricle Volume Estimation using Stochastic Differential Equations
  • 16:50-17:10 - Semih Cayci (RWTH, CRC 1481): A Neural Tangent Kernel Analysis of Adaptively-Regularized Gauss–Newton in Deep Learning
  • 17:15-17:35 - Michael Herty (RWTH, CRC 1481): Uncertainty Quantification For Nonlinear Hyperbolic Balance Laws
  • 17:35-17:55 - Mathias Oster (RWTH, CRC 1481): Using Variationally Correct Residuals as Loss Functions in Neural Network Learning for Parametrized PDEs

 

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