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Jonas Latz
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Can physics-informed neural networks beat the finite element method?
TG Grossmann, UJ Komorowska, J Latz, CB Schönlieb
IMA Journal of Applied Mathematics 89 (1), 143-174, 2024
962024
On the well-posedness of Bayesian inverse problems
J Latz
SIAM/ASA Journal on Uncertainty Quantification 8 (1), 451-482, 2020
672020
Multilevel Sequential² Monte Carlo for Bayesian inverse problems
J Latz, I Papaioannou, E Ullmann
Journal of Computational Physics 368, 154-178, 2018
632018
Analysis of Stochastic Gradient Descent in Continuous Time
J Latz
Statistics and Computing 31, 39, 2021
452021
Fast sampling of parameterised Gaussian random fields
J Latz, M Eisenberger, E Ullmann
Computer Methods in Applied Mechanics and Engineering 348, 978-1012, 2019
282019
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
272020
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
252019
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
192021
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
162020
Generalized parallel tempering on Bayesian inverse problems
J Latz, JP Madrigal-Cianci, F Nobile, R Tempone
Statistics and Computing 31 (5), 67, 2021
152021
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
102021
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
82020
Bayesian inverse problems are usually well-posed
J Latz
SIAM Review 65 (3), 831-865, 2023
72023
Subsampling in ensemble Kalman inversion
M Hanu, J Latz, C Schillings
Inverse Problems 39, 094002, 2023
62023
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
62023
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
62021
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
52020
Bayes Linear Methods for Inverse Problems
J Latz
Master’s thesis, University of Warwick, 2016
52016
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
22023
Losing momentum in continuous-time stochastic optimisation
K Jin, J Latz, C Liu, A Scagliotti
arXiv preprint arXiv:2209.03705, 2022
22022
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