Reading Group Uncertainty Quantification


The reading group meets every other week to discuss current research papers on the topic Uncertainty Quantification. The discussion is lead by a moderator. Everybody is welcome to attend. Subscription to the group's mailing list is possible here.

Important information: We meet and discuss ONLINE using Zoom. The moderator creates a meeting link and password, and distributes it via the group mailing list.

Winter term 2022/23

Date Time Moderator Topic
21.03.23 16:00 Jonas Latz (Edinburgh) Can Physics-Informed Neural Networks beat the Finite element Method?
20.12.22 16:00 Leila Taghizadeh (TUM) A-Optimal Active Learning
06.12.22 16:00 Anastasia Istratuca (Edinburgh) Effective Generation of Compressed Stationary Gaussian Fields
29.11.22 16:00 Elisabeth Ullmann (TUM) Global sensitivity analysis using derivative-based sparse Poincaré chaos expansions

  Date Time Moderator Topic  
  05.07.22 16:00 Laura Scarabosio (Radboud) Data driven gradient flows  
  21.06.22 16:00 Daniel Walter (Linz) Sparse solutions in optimal control of PDEs with uncertain parameters: the linear case  
  07.06.22 16:00 Abdul-Lateef Haji-Ali (Heriot-Watt, Edinburgh) Unbiased Multilevel Monte Carlo methods for intractable distributions: MLMC meets MCMC  
  24.05.22 16:00 Giovanni Rabitti (Heriot-Watt, Edinburgh) On Shapley Value for Measuring Importance of Dependent Inputs  
  10.05.22 16:00 Florian Beiser (NTNU) Combining data assimilation and machine learning to infer unresolved scale parametrization  

  Date Time Moderator Topic  
  08.02.22 16:00 Felipe Uribe (DTU, Copenhagen) Unbiased Markov chain Monte Carlo with couplings  
  25.01.22 16:00 Björn Sprungk (Freiberg) Analysis of a class of Multi-Level Markov Chain Monte Carlo algorithms based on Independent Metropolis-Hastings  
  11.01.22 16:00 Jonas Latz (Heriot-Watt, Edinburgh) Probability, Frequency and Reasonable Expectation  
  07.12.21 16:00 Leila Taghizadeh (TUM) Optimal experimental design under irreducible uncertainty for linear inverse problems governed by PDEs  
  23.11.21 16:00 Fabian Wagner (TUM) Physics-Informed Machine Learning with Conditional Karhunen-Loève Expansions  
  09.11.21 16:00 Elisabeth Ullmann (TUM) Unbiased MLMC-based variational Bayes for likelihood-free inference  
  26.10.21 16:00 Simon Urbainczyk (Heriot-Watt, Edinburgh) Analysis of boundary effects on PDE-based sampling of Whittle-Matérn random fields  
  12.10.21 16:00 Jan Stanczuk (Cambridge) Score-Based Generative Modeling through Stochastic Differential Equations  

  Date Time Moderator Topic  
  20.07.21 16:00 Simon Weißmann (Heidelberg) Consensus Based Sampling  
  06.07.21 16:00 Laura Scarabosio (Radboud) Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs  
  22.06.21 16:00 Fabian Wagner (TUM) Complete dynamics and spectral decomposition of the Ensemble Kalman Inversion  
  08.06.21 16:00 Jonas Latz (Cambridge) A dynamical systems framework for intermittent data assimilation  
  25.05.21 16:00 Daniel Schaden (TUM) On quantitative stability in infinite-dimensional optimization under uncertainty  
  11.05.21 16:00 Sam Power (Bristol) Transport map accelerated Markov chain Monte Carlo  
  27.04.21 16:00 Philipp Eisenhauer (Bonn) Structural models for policy-making: Coping with parametric uncertainty  

  Date Time Moderator Topic  
  07.07.20 16:00 Simon Weißmann (Mannheim) Tikhonov Regularization Within Ensemble Kalman Inversion  
  23.06.20 16:00 Florian Beiser (TUM) Risk-Averse PDE-Constrained Optimization Using the Conditional Value-At-Risk  
  09.06.20 16:00 Robert Scheichl (Heidelberg) On the geometry of Stein variational gradient descent
  26.05.20 16:00 Daniel Schaden (TUM) Continuous Level Monte Carlo and Sample-Adaptive Model Hierarchies
  12.05.20 16:00 Felipe Uribe (DTU, Copenhagen) Cauchy difference priors for edge-preserving Bayesian inversion
  28.04.20 16:00 Jonas Latz (Cambridge) Calibrate, Emulate, Sample

  Date Time Room Moderator Topic  
  28.01.20 16:00 02.10.011 Fabian Wagner Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
  14.01.20 16:45 02.10.011 Simon Urbainczyk Adaptive Sampling Strategies for Stochastic Optimization
  17.12.19 16:00 02.10.011 Ustim Khristenko Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification
  19.11.19 16:00 02.10.011 Mario Parente The Lipschitz matrix: a tool for parameter space dimension reduction
  05.11.19 16:00 02.10.011 Fabian Wagner Estimation of small failure probabilities in high dimensions by subset simulation
  22.10.19 16:00 02.10.011 Jonas Latz Why Are Big Data Matrices Approximately Low Rank?

  Date Time Room Moderator Topic
  17.11.16 10:00 03.11.018 Jonas Latz (M2) Stuart - Uncertainty Quantification in Bayesian Inversion  
  08.12.16 10:15 03.11.018 Elisabeth Ullmann (M2) Stuart(2010) - Inverse Problems: A Bayesian Perspective (§6)
  15.12.16 10:15 03.11.018 Elisabeth Ullmann (M2) Stuart(2010) - Inverse Problems: A Bayesian Perspective (§6)
  19.01.17 10:15 03.11.018 Tinsley Oden (ICES) OPAL: the Occam Plausibility Algorithm for Bayesian Model Selection and Validation
  02.02.17 10:15 03.11.018 Steven Mattis (M2) Stuart(2010) - Inverse Problems: A Bayesian Perspective (§1-3)
  22.02.17 14:15 - Jonas Latz (M2) Stuart(2010) - Inverse Problems: A Bayesian Perspective (§4-5)