Jonas Latz
Cited by
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Multilevel Sequential² Monte Carlo for Bayesian inverse problems
J Latz, I Papaioannou, E Ullmann
Journal of Computational Physics 368, 154-178, 2018
On the well-posedness of Bayesian inverse problems
J Latz
SIAM/ASA Journal on Uncertainty Quantification 8 (1), 451-482, 2020
Analysis of Stochastic Gradient Descent in Continuous Time
J Latz
Statistics and Computing 31, 39, 2021
Fast sampling of parameterised Gaussian random fields
J Latz, M Eisenberger, E Ullmann
Computer Methods in Applied Mechanics and Engineering 348, 978-1012, 2019
Can physics-informed neural networks beat the finite element method?
TG Grossmann, UJ Komorowska, J Latz, CB Schönlieb
arXiv preprint arXiv:2302.04107, 2023
Multilevel sequential importance sampling for rare event estimation
F Wagner, J Latz, I Papaioannou, E Ullmann
SIAM Journal on Scientific Computing 42 (4), A2062-A2087, 2020
Bayesian parameter identification in Cahn--Hilliard models for biological growth
C Kahle, KF Lam, J Latz, E Ullmann
SIAM/ASA Journal on Uncertainty Quantification 7 (2), 526-552, 2019
Classification and image processing with a semi-discrete scheme for fidelity forced Allen--Cahn on graphs
J Budd, Y van Gennip, J Latz
GAMM-Mitteilungen 44 (1), e202100004, 2021
Generalized parallel tempering on Bayesian inverse problems
J Latz, JP Madrigal-Cianci, F Nobile, R Tempone
Statistics and Computing 31 (5), 67, 2021
Multilevel adaptive sparse Leja approximations for Bayesian inverse problems
IG Farcas, J Latz, E Ullmann, T Neckel, HJ Bungartz
SIAM Journal on Scientific Computing 42 (1), A424-A451, 2020
Bayesian inference with subset simulation in varying dimensions applied to the Karhunen–Loève expansion
F Uribe, I Papaioannou, J Latz, W Betz, E Ullmann, D Straub
International Journal for Numerical Methods in Engineering 122 (18), 5100-5127, 2021
Certified and fast computations with shallow covariance kernels
D Kressner, J Latz, S Massei, E Ullmann
Foundations of Data Science 2 (4), 487-512, 2020
Error analysis for probabilities of rare events with approximate models
F Wagner, J Latz, I Papaioannou, E Ullmann
SIAM Journal on Numerical Analysis 59 (4), 1948-1975, 2021
Bayes Linear Methods for Inverse Problems
J Latz
Master’s thesis, University of Warwick, 2016
A continuous-time stochastic gradient descent method for continuous data
K Jin, J Latz, C Liu, CB Schönlieb
Journal of Machine Learning Research 24 (274), 1−48, 2023
Bayesian model inference od random fields represented with the karahunen-loéve expansion
F Uribe, I Papaiannou, W Betz, J Latz, D Straub
UNCECOMP 2017 2 nd ECCOMAS Thematic Conference on Uncertainty Quantification …, 2017
Joint reconstruction-segmentation on graphs
J Budd, Y van Gennip, J Latz, S Parisotto, CB Schönlieb
SIAM Journal on Imaging Sciences 16 (2), 911-947, 2023
Gradient flows and randomised thresholding: sparse inversion and classification
J Latz
Inverse Problems 38, 124006, 2022
Improving a Stochastic Algorithm for Regularized PET Image Reconstruction
C Delplancke, M Gurnell, J Latz, PJ Markiewicz, CB Schönlieb, ...
2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC …, 2020
A practical example for the non-linear Bayesian filtering of model parameters
M Bulté, J Latz, E Ullmann
Quantification of Uncertainty: Improving Efficiency and Technology: QUIET …, 2020
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