Jonas Latz

439 total citations · 1 hit paper
17 papers, 220 citations indexed

About

Jonas Latz is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Artificial Intelligence. According to data from OpenAlex, Jonas Latz has authored 17 papers receiving a total of 220 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Statistics and Probability, 7 papers in Statistics, Probability and Uncertainty and 5 papers in Artificial Intelligence. Recurrent topics in Jonas Latz's work include Probabilistic and Robust Engineering Design (7 papers), Markov Chains and Monte Carlo Methods (5 papers) and Statistical Methods and Inference (5 papers). Jonas Latz is often cited by papers focused on Probabilistic and Robust Engineering Design (7 papers), Markov Chains and Monte Carlo Methods (5 papers) and Statistical Methods and Inference (5 papers). Jonas Latz collaborates with scholars based in United Kingdom, Germany and United States. Jonas Latz's co-authors include Elisabeth Ullmann, Carola‐Bibiane Schönlieb, Iason Papaioannou, Kei Fong Lam, Fabian Wagner, Claudia Schillings, Raúl Tempone, Wolfgang Betz, Hans‐Joachim Bungartz and Fabio Nobile and has published in prestigious journals such as Journal of Computational Physics, Computer Methods in Applied Mechanics and Engineering and IEEE Transactions on Medical Imaging.

In The Last Decade

Jonas Latz

15 papers receiving 209 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Can physics-informed neural networks beat the finite element method?

2024 75 citations Jonas Latz, Carola‐Bibiane Schönlieb et al. IMA Journal of Applied Mathematics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Jonas Latz United Kingdom	8	61	57	51	48	30	17	220
Chad Lieberman United States	7	126 2.1×	102 1.8×	51 1.0×	22 0.5×	40 1.3×	9	273
Bamdad Hosseini United States	7	73 1.2×	35 0.6×	68 1.3×	16 0.3×	45 1.5×	23	217
Alexey Chernov Germany	11	29 0.5×	105 1.8×	40 0.8×	20 0.4×	91 3.0×	40	322
Giovanni Migliorati Switzerland	7	57 0.9×	165 2.9×	15 0.3×	17 0.4×	55 1.8×	12	224
Qifeng Liao China	11	197 3.2×	108 1.9×	55 1.1×	7 0.1×	105 3.5×	34	368
Stefano Pagani Italy	13	126 2.1×	60 1.1×	33 0.6×	4 0.1×	53 1.8×	23	337
Hans-Jörg Starkloff Germany	5	67 1.1×	244 4.3×	14 0.3×	15 0.3×	41 1.4×	7	318
Giampietro Allasia Italy	10	27 0.4×	9 0.2×	23 0.5×	29 0.6×	114 3.8×	37	273
Leo Wai-Tsun Ng United States	5	83 1.4×	257 4.5×	17 0.3×	9 0.2×	43 1.4×	7	332
Yiping Lu China	5	260 4.3×	67 1.2×	101 2.0×	3 0.1×	93 3.1×	17	386

Countries citing papers authored by Jonas Latz

Since Specialization

Citations

This map shows the geographic impact of Jonas Latz's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jonas Latz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonas Latz more than expected).

Fields of papers citing papers by Jonas Latz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonas Latz. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jonas Latz. The network helps show where Jonas Latz may publish in the future.

Co-authorship network of co-authors of Jonas Latz

This figure shows the co-authorship network connecting the top 25 collaborators of Jonas Latz. A scholar is included among the top collaborators of Jonas Latz based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jonas Latz. Jonas Latz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

17 of 17 papers shown

1.

Bertozzi, Andrea L., et al.. (2025). Partial differential equations in data science. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. 383(2298). 20240249–20240249.

2.

Ezhov, Ivan, Florian Kofler, Jana Lipková, et al.. (2024). A Learnable Prior Improves Inverse Tumor Growth Modeling. IEEE Transactions on Medical Imaging. 44(3). 1297–1307. 3 indexed citations

3.

Latz, Jonas, et al.. (2024). Can physics-informed neural networks beat the finite element method?. IMA Journal of Applied Mathematics. 89(1). 143–174. 75 indexed citations breakdown →

4.

Latz, Jonas, et al.. (2023). Subsampling in ensemble Kalman inversion. Inverse Problems. 39(9). 94002–94002. 2 indexed citations

5.

Latz, Jonas. (2023). Bayesian Inverse Problems Are Usually Well-Posed. SIAM Review. 65(3). 831–865. 7 indexed citations

6.

Gennip, Yves van, et al.. (2023). Joint Reconstruction-Segmentation on Graphs. SIAM Journal on Imaging Sciences. 16(2). 911–947. 1 indexed citations

7.

Latz, Jonas. (2022). Gradient flows and randomised thresholding: sparse inversion and classification*. Inverse Problems. 38(12). 124006–124006.

8.

Latz, Jonas, et al.. (2021). Generalized parallel tempering on Bayesian inverse problems. Statistics and Computing. 31(5). 5 indexed citations

9.

Papaioannou, Iason, et al.. (2021). Bayesian inference with subset simulation in varying dimensions applied to the Karhunen–Loève expansion. International Journal for Numerical Methods in Engineering. 122(18). 5100–5127. 3 indexed citations

10.

Latz, Jonas. (2021). Analysis of stochastic gradient descent in continuous time. Statistics and Computing. 31(4). 18 indexed citations

11.

Latz, Jonas, et al.. (2020). Multilevel Adaptive Sparse Leja Approximations for Bayesian Inverse Problems. SIAM Journal on Scientific Computing. 42(1). A424–A451. 6 indexed citations

12.

Gurnell, Mark, et al.. (2020). Improving a Stochastic Algorithm for Regularized PET Image Reconstruction. 1–3. 1 indexed citations

13.

Wagner, Fabian, Jonas Latz, Iason Papaioannou, & Elisabeth Ullmann. (2020). Multilevel Sequential Importance Sampling for Rare Event Estimation. SIAM Journal on Scientific Computing. 42(4). A2062–A2087. 13 indexed citations

14.

Latz, Jonas. (2020). On the Well-posedness of Bayesian Inverse Problems. SIAM/ASA Journal on Uncertainty Quantification. 8(1). 451–482. 25 indexed citations

15.

Lam, Kei Fong, et al.. (2019). Bayesian Parameter Identification in Cahn--Hilliard Models for Biological Growth. SIAM/ASA Journal on Uncertainty Quantification. 7(2). 526–552. 12 indexed citations

16.

Latz, Jonas, et al.. (2019). Fast sampling of parameterised Gaussian random fields. Computer Methods in Applied Mechanics and Engineering. 348. 978–1012. 19 indexed citations

17.

Latz, Jonas, Iason Papaioannou, & Elisabeth Ullmann. (2018). Multilevel Sequential2 Monte Carlo for Bayesian inverse problems. Journal of Computational Physics. 368. 154–178. 30 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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