Research Seminar Numerical Analysis of Stochastic and Deterministic Partial Differential Equations   📅

Institute
Head
Claudia Schillings
Description
The seminar brings together experts on numerical analysis, applied mathematics, statistics and stochastics with particular focus on applications to stochastic and deterministic partial differential equations.
Number of talks
57
Thu, 20.06.24
Solving the Optimal Experiment Design Problem with mixed-integer convex methods
Thu, 13.06.24
room 126, Arnimal...
Optimisation in Bayesian experimental design
Thu, 06.06.24
room 126, Arnimal...
Quasi-Monte Carlo Methods for PDEs on Random Domains
Thu, 30.05.24
room 126, Arnimal...
Learning Operators via Hypernetworks
Thu, 23.05.24
room 210, Arnimal...
Sampling in Unit Time with Kernel Fisher-Rao Flow Paper von Aimee Maurais und Youssef Marzouk
Thu, 16.05.24
room 210, Arnimal...
QMC meets Optimal sampling
Thu, 08.02.24 at 13:00
A3/115
Thu, 01.02.24 at 13:00
room A6/108
Towards optimal sensor placement for inverse problems in spaces of measures
Thu, 01.02.24 at 12:00
A6/108
PDE-Constrained Optimization Problems with Probabilistic State Constraints
Thu, 11.01.24 at 13:00
room A3/115
Thu, 21.12.23 at 13:00
room A3/115
From Probabilistic Models of Mechanical Failure to Multi-Objective Shape Optimization
Fri, 01.12.23 at 10:00
A6 108/109
Ensemble Kalman filtering for epistemic uncertainty
Thu, 30.11.23 at 13:00
A3/115
An introduction to TorchPhysics: Deep Learning for partial differential equations
Thu, 16.11.23 at 13:00
room A6/108
An optimal control perspective on diffusion-based generative modeling leading to robust numerical methods
Thu, 02.11.23 at 13:00
room TBA
Effiziente Synergien durch integrierte Prozessoptimierung – Bedarfsgerechter Einsatz von ProduktionskapazitĂ€ten unter BerĂŒcksichtigung der partiellen Produktionssysteme
Thu, 02.11.23 at 12:00
A6/210
Ensemble Kalman Inversion for time-dependent forward operators
Thu, 26.10.23 at 13:00
room A6/108
On polynomial-time mixing for high-dimensional MCMC in inverse problems
Tue, 17.10.23 at 15:15
A6/108
Edge-preserving inversion with heavy-tailed Bayesian neural networks priors
Wed, 30.08.23 at 10:15
A6/108/109
Analysis of vector-valued random features
Wed, 19.07.23 at 15:30
A6/108/109
A random dynamical system perspective on chemical reaction networks
Wed, 19.07.23 at 15:00
A6/108/109
Bayesian inversion with alpha-stable priors
Wed, 19.07.23 at 14:15
A6/108/109
Ensemble-based Data Assimilation for high-dimensional nonlinear dynamical systems
Fri, 07.07.23 at 11:00
A6/108/109
On definitions of modes and MAP estimators
Fri, 30.06.23 at 10:15
A6/108/109
Improving Ensemble Kalman Filter performance by adaptively controlling the ensemble
Thu, 22.06.23 at 14:15
A6/126
Optimal Control and Feedback Stabilization Under Uncertainty
Fri, 16.06.23 at 11:00
A6/108/109
Approximating Multivariate Functions with Embedded Lattice-based Algorithms
Fri, 16.06.23 at 10:15
A6/108/109
A randomised lattice algorithm for integration using a ïŹxed generating vector
Fri, 16.06.23 at 09:30
A6/108/109
Energy, Discrepancy, and Polarization of Greedy Sequences on the Sphere
Mon, 05.06.23 at 13:00
A6/210
Subgaussian concentration in Hilbert spaces and inference in inverse problems
Mon, 05.06.23 at 12:15
A6/108/109
Approximation of SDEs with irregular drift: stochastic sewing approach
Fri, 05.05.23 at 10:15
A6/210
Wed, 26.04.23 at 14:15
A6/108/109
A randomized operator splitting scheme inspired by stochastic optimization methods
Fri, 21.04.23 at 10:15
A6/108/109
Langevin Dynamics: Bayesian inference, homotopy and generative modeling
Mon, 23.01.23 at 10:15
A6/009
Strong approximation of the CIR process: A never ending story?
Wed, 11.01.23 at 11:00
A6/108/109
Higher order methods for geometric inverse problems
Wed, 11.01.23 at 10:15
A6/108/109
Shape optimization for time-dependent domains
Mon, 05.12.22 at 10:15
A6/009
Why rough stuff matters for UQ
Mon, 28.11.22 at 10:15
A6/009
Optimal and Bayesian hypothesis testing in statistical inverse problems
Wed, 23.11.22 at 14:15
A6/108/109
Multiobjective Learning in Solar Energy Prediction: Benefits and Algorithms
Mon, 14.11.22 at 10:15
A6/009
Frechet derivatives of path functionals of stochastic differential equations
Mon, 07.11.22 at 10:15
online
Approximating distribution functions in uncertainty quantification using quasi-Monte Carlo methods
Mon, 31.10.22 at 11:00
A6/009
Cauchy Markov random field priors for Bayesian inversion
Mon, 31.10.22 at 10:15
A6/009
Short-term vital parameter forecasting in the intensive care unit
Fri, 21.10.22 at 10:15
A6/126
Eigenlocking – Parameter-dependent loss of convergence rate
Mon, 17.10.22 at 11:00
A6/009
Nonparametric approximation of conditional expectation operators
Mon, 17.10.22 at 10:15
A6/009
QMC and sparse grids beyond uniform distributions on cubes: transport maps to mixture distributions
Fri, 15.07.22 at 10:15
A6/108/109
Introduction to DG methods
Fri, 08.07.22 at 11:00
A6/108/109
Fri, 08.07.22 at 10:15
A6/108/109
On tensor-based training of neural networks
Fri, 01.07.22 at 10:15
online
Fri, 10.06.22 at 10:15
online
Fri, 03.06.22 at 10:15
A6/108/109
QMC and kernel interpolation
Fri, 27.05.22 at 10:15
online
Data-based modeling of the cellular response to oxidative stress -- A Bayesian approach for model selection and parameter identification in (bio)chemical networks
Fri, 20.05.22 at 10:15
online
Fri, 13.05.22 at 10:15
A6/108/109
Gaussian processes for uncertainty quantification and error estimation
Fri, 06.05.22 at 10:15
A6/108/109
MAP estimators in l^p
Fri, 29.04.22 at 10:15
A6/108/109
Variational inference for Bayesian neural networks