Reading Group Uncertainty Quantification
The reading group meets in the Maths/Computer Science building on the Garching campus. Everybody is welcome to attend. Subscription to the group's mailing list is possible here.Sommersemester 2020
Important information: Since the buildings on campus are closed for teaching, we will meet and discuss ONLINE using BigBlueButton. The moderator will create a meeting link and password, and distribute it via the group mailing list.| 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 |
Wintersemester 2019/20
| 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? |
Sommersemester 2019
| Date | Time | Room | Moderator | Topic |
|---|---|---|---|---|
| 30.07.19 | 16:00 | 02.10.011 | Yannik Schälte (ICB) | ABC Samplers |
| 16.07.19 | 16:00 | 02.10.011 | Brendan Keith (M2) | Optimization of PDEs with uncertain inputs |
| 25.06.19 | 16:00 | 02.10.011 | Florian Beiser (M2) | A stochastic gradient method with mesh refinement for PDE constrained optimization under uncertainty |
| 18.06.19 | 16:00 | 02.10.011 | Mario Parente (M2) | A transport-based multifidelity preconditioner for Markov chain Monte Carlo |
| 28.05.19 | 16:00 | 02.10.011 | Laura Scarabosio (M2) | A Multiscale Strategy for Bayesian Inference Using Transport Maps |
| 14.05.19 | 16:15 | 02.10.011 | Elisabeth Ullmann (M2) | Conditional Karhunen-Loeve expansion for uncertainty quantification and active learning in partial differential equation models |
| 30.04.19 | 16:15 | 02.10.011 | Ionut-Gabriel Farcas (I5) | Sensitivity-driven adaptive sparse stochastic approximations in plasma microinstability analysis |
Wintersemester 2018/19
| Date | Time | Room | Moderator | Topic |
|---|---|---|---|---|
| 19.02.19 | 16:15 | 02.10.011 | Konstantin Riedl (TUM) | Multifidelity Preconditioning of the Cross-Entropy Method for Rare Event Simulation and Failure Probability Estimation |
| 05.02.19 | 16:15 | 02.10.011 | Jonas Latz (M2) | How Deep are Deep Gaussian processes? |
| 22.01.19 | 16:15 | 02.10.011 | Fabian Wagner (M2) | Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations |
| 08.01.19 | 16:15 | 02.10.011 | Steven Mattis (M2) | A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems |
| 20.11.18 | 16:15 | 02.10.011 | Ustim Khristenko (M2) | Analysis of boundary effects on PDE-based sampling of Whittle-Matérn random fields |
| 06.11.18 | 16:15 | 02.10.011 | Laura Scarabosio (M2) | Well-posed Bayesian geometric inverse problems arising in subsurface flow |
| 23.10.18 | 16:15 | 02.10.011 | Mario Parente (M2) | A probabilistic framework for approximating functions in the active subspace |
Sommersemester 2018
| Date | Time | Room | Moderator | Topic |
|---|---|---|---|---|
| 24.07.18 | 16:15 | 02.10.011 | Sabrina Balzer (M2) | Posterior Consistency for Gaussian Process Approximations of Bayesian Posterior Distributions |
| 10.07.18 | 16:15 | 02.10.011 | Mario Parente (M2) | Importance Sampling and Necessary Sample Size: An Information Theory Approach |
| 26.06.18 | 16:15 | 02.10.011 | Daniel Schaden (M2) | MCMC Methods for Functions: Modifying Old Algorithms to Make Them Faster |
| 12.06.18 | 16:15 | 02.10.011 | Ludger Pähler (TUM) | PDE-Net: Learning PDEs from Data |
| 29.05.18 | 16:15 | 02.10.011 | Elisabeth Ullmann (M2) | Efficient white noise sampling and coupling for multilevel Monte Carlo with non-nested meshes |
| 15.05.18 | 16:15 | 02.10.011 | Jonas Latz (M2) | Deep Learning: An Introduction for Applied Mathematicians |
| 24.04.18 | 16:15 | 02.10.011 | Steven Mattis (M2) | Combining Push-Forward Measures and Bayes' Rule to Construct Consistent Solutions to Stochastic Inverse Problems |
Wintersemester 2017/18
| Date | Time | Room | Moderator | Topic |
|---|---|---|---|---|
| 30.01.18 | 16:15 | 02.10.011 | Elisabeth Ullmann (M2) | Machine learning of differential equations using Gaussian processes (Paper 1) (Paper 2) |
| 16.01.18 | 16:15 | 02.10.011 | Jonas Latz (M2) | Statistical analysis of differential equations: introducing probability measures on numerical solutions |
| 05.12.17 | 16:15 | 02.10.011 | Steven Mattis (M2) | A Short Course on Duality, Adjoint Operators, Green’s Functions, and A Posteriori Error Analysis (§3-4) |
| 21.11.17 | 16:15 | 02.10.011 | Steven Mattis (M2) | A Short Course on Duality, Adjoint Operators, Green’s Functions, and A Posteriori Error Analysis (§1-2) |
| 07.11.17 | 16:15 | 02.10.011 | Laura Scarabosio (M2) | Finite element methods for semilinear elliptic stochastic partial differential equations |
| 24.10.17 | 16:15 | 02.10.011 | Piotr Swierczynski (M2) | An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach |
Sommersemester 2017
| Date | Time | Room | Moderator | Topic | Slides |
|---|---|---|---|---|---|
| 12.04.17 | 14:00 | 02.08.011 | Laura Scarabosio (M2) | A Fresh Look at the Kalman Filter | |
| 02.05.17 | 16:15 | 03.06.011 | Mario Parente (M2) | Ensemble Kalman methods for inverse problems | pdf, proofs |
| 16.05.17 | 16:15 | 03.06.011 | Ionut Farcas (CS) | An efficient Bayesian inference approach to inverse problems based on an adaptive sparse grid collocation method | |
| 06.06.17 | 16:15 | 03.06.011 | Jonas Latz (M2) | Analysis of the ensemble Kalman filter for inverse problems | |
| 27.06.17 | 16:15 | 03.06.011 | Elisabeth Ullmann (M2) | On the Brittleness of Bayesian Inference | |
| 11.07.17 | 16:15 | 03.06.011 | Mario Parente (M2) | Active Subspaces: Emerging Ideas for Dimension Reduction in Parameter Studies (§1-3) | |
| 25.07.17 | 16:15 | 03.06.011 | Steven Mattis (M2) | Active Subspaces: Emerging Ideas for Dimension Reduction in Parameter Studies (§4-6) | |
| 23.08.17 | 10:00 | 03.06.011 | Daniel Schaden (M2) | Optimal Model Management for Multifidelity Monte Carlo Estimation |
Wintersemester 2016/17
| Date | Time | Room | Moderator | Topic | Slides | Further Material | |
|---|---|---|---|---|---|---|---|
| 17.11.16 | 10:00 | 03.11.018 | Jonas Latz (M2) | Stuart - Uncertainty Quantification in Bayesian Inversion | Bayesian Statistics | ||
| 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) | Random fields | ||
| 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) | Bayesian Inverse | ||
| 22.02.17 | 14:15 | - | Jonas Latz (M2) | Stuart(2010) - Inverse Problems: A Bayesian Perspective (§4-5) | Well-posed problems, Algorithms | GitHub Implementations | |
