Publications

Preprints

[13] Daniel Kressner, Jonas Latz, Stefano Massei, Elisabeth Ullmann (2020): Certified and fast computations with shallow covariance kernels. Under review. (.bib, arXiv)

[12] Fabian Wagner, Jonas Latz, Iason Papaioannou, Elisabeth Ullmann (2019): Multilevel Sequential Importance Sampling for Rare Event Estimation. Under review. (.bib, arXiv)

[11] Felipe Uribe, Iason Papaioannou, Jonas Latz, Wolfgang Betz, Elisabeth Ullmann, Daniel Straub (2019): Bayesian inference with subset simulation in spaces of varying dimension. Under review. (.bib, full text)


Refereed journal articles and book chapters

[10] Ionuţ-Gabriel Farcaş, Jonas Latz, Elisabeth Ullmann, Tobias Neckel, Hans-Joachim Bungartz (2020): Multilevel Adaptive Sparse Leja Approximations for Bayesian Inverse Problems. SIAM J. Sci. Comput. 42(1), p. A424–A451, doi. (.bib, arXiv)

[9] Jonas Latz (2019): On the well-posedness of Bayesian inverse problems. Accepted in SIAM/ASA J. Uncertain. Quantif. (.bib, arXiv)

[8] Christian Kahle, Kei Fong Lam, Jonas Latz, Elisabeth Ullmann (2019): Bayesian parameter identification in Cahn-Hilliard models for biological growth. SIAM/ASA J. Uncertain. Quantif. 7(2), p. 526-552, doi. (.bib, arXiv)

[7] Jonas Latz, Marvin Eisenberger, Elisabeth Ullmann (2019): Fast Sampling of parameterised Gaussian random fields. Comput. Methods in Appl. Mech. Engrg. 348, p. 978-1012, doi. (.bib, arXiv)

[6] Matthieu Bulté, Jonas Latz, Elisabeth Ullmann (2018): A practical example for the non-linear Bayesian filtering of model parameters. Accepted in M. D’Elia, M. Gunzburger, G. Rozza (ed.): Quantification of Uncertainty: Improving Efficiency and Technology - QUIET selected contributions, Lecture Notes in Computational Science and Engineering, Springer, Cham. (github, .bib, arXiv)

[5] Jonas Latz, Iason Papaioannou, Elisabeth Ullmann (2018): Multilevel Sequential² Monte Carlo for Bayesian Inverse Problems. J. Comput. Phys. 368, p. 154-178, doi. (.bib, arXiv)


Theses

[4] Jonas Latz (2019): Exploring and exploiting hierarchies in Bayesian inverse problems. Doctoral thesis, Technical University of Munich. (.bib, full text)

[3] Jonas Latz (2016): Bayes Linear Methods for Inverse Problems. Master’s thesis, University of Warwick. (.bib, full text)

[2] Jonas Latz (2014): Äußere Hausdorff-Maße: Anwendungen und Eigenschaften. Bachelor’s thesis, University of Trier (in German).


Miscellaneous (non-refereed)

[1] Jonas Latz (2019): On the well-posedness of Bayesian inverse problems: The Gaussian noise case. Oberwolfach Report 12/2019, p. 35-36, doi. (preliminary full text)