| 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 | 81 | 2024 |
| On the well-posedness of Bayesian inverse problems J Latz SIAM/ASA Journal on Uncertainty Quantification 8 (1), 451-482, 2020 | 65 | 2020 |
| Multilevel Sequential² Monte Carlo for Bayesian inverse problems J Latz, I Papaioannou, E Ullmann Journal of Computational Physics 368, 154-178, 2018 | 62 | 2018 |
| Analysis of Stochastic Gradient Descent in Continuous Time J Latz Statistics and Computing 31, 39, 2021 | 43 | 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 | 28 | 2019 |
| 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 | 25 | 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 | 24 | 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 | 16 | 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 | 15 | 2020 |
| Generalized parallel tempering on Bayesian inverse problems J Latz, JP Madrigal-Cianci, F Nobile, R Tempone Statistics and Computing 31 (5), 67, 2021 | 13 | 2021 |
| 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 | 9* | 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 | 8 | 2020 |
| Bayesian inverse problems are usually well-posed J Latz SIAM Review 65 (3), 831-865, 2023 | 7 | 2023 |
| 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 | 6 | 2023 |
| Subsampling in ensemble Kalman inversion M Hanu, J Latz, C Schillings Inverse Problems 39, 094002, 2023 | 5 | 2023 |
| 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 | 5 | 2021 |
| Bayes Linear Methods for Inverse Problems J Latz Master’s thesis, University of Warwick, 2016 | 5 | 2016 |
| 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 | 4 | 2020 |
| 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 | 2 | 2023 |
| Losing momentum in continuous-time stochastic optimisation K Jin, J Latz, C Liu, A Scagliotti arXiv preprint arXiv:2209.03705, 2022 | 2 | 2022 |
Elisabeth UllmannTechnical University of MunichVerified email at tum.de
