Jonas Latz
Impact in
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- Probabilistic and Robust Engineering Design
- Statistics and Probability top 5%
- Markov Chains and Monte Carlo Methods
- Statistical Methods and Inference
Papers in
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- Markov Chains and Monte Carlo Methods 6
- Statistical Methods and Inference 5
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- Probabilistic and Robust Engineering Design 7
- Co-authors
- Elisabeth Ullmann (6 shared papers)Carola‐Bibiane Schönlieb (3 shared papers)Iason Papaioannou (3 shared papers)Fabian Wagner (1 shared paper)Kei Fong Lam (1 shared paper)Raúl Tempone (1 shared paper)Tobias Neckel (1 shared paper)Fabio Nobile (1 shared paper)
- Journals
- Inverse Problems (3 papers)SIAM Journal on Scientific Computing (3 papers)SIAM/ASA Journal on Uncertainty Quantification (2 papers)Statistics and Computing (2 papers)SIAM Review (1 paper)
- Partner nations
- United KingdomGermanySwitzerland
In The Last Decade
Jonas Latz
15 papers receiving 230 citations
Jonas Latz's Hit Papers
Peers
Comparison fields: 5 of 59
- Statistics, Probability and Uncertainty 57
- Statistics and Probability 48
- Statistical and Nonlinear Physics 66
- Computational Mathematics 2
- Numerical Analysis 11
Countries citing papers authored by Jonas Latz
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
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-authors
The 25 scholars most cited alongside Jonas Latz, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Can physics-informed neural networks beat the finite element method? Hit paper breakdown → | 2024 | 88 |
| 2 | 2018 | 30 | |
| 3 | 2020 | 25 | |
| 4 | 2019 | 19 | |
| 5 | 2021 | 18 | |
| 6 | 2020 | 13 | |
| 7 | 2019 | 13 | |
| 8 | 2023 | 9 | |
| 9 | 2020 | 6 | |
| 10 | 2021 | 5 | |
| 11 | 2021 | 3 | |
| 12 | 2024 | 3 | |
| 13 | 2023 | 2 | |
| 14 | 2023 | 1 | |
| 15 | 2020 | 1 | |
| 16 | 2025 | 0 | |
| 17 | 2024 | 0 | |
| 18 | 2022 | 0 | |
| 19 | 2025 | 0 |
About Jonas Latz
Jonas Latz is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty, Artificial Intelligence, Statistical and Nonlinear Physics and Mathematical Physics, having authored 19 papers that have together received 236 indexed citations. Recurring topics across this work include Probabilistic and Robust Engineering Design (7 papers), Markov Chains and Monte Carlo Methods (6 papers), Statistical Methods and Inference (5 papers), Gaussian Processes and Bayesian Inference (5 papers), Mathematical Biology Tumor Growth (2 papers), Numerical methods in inverse problems (2 papers), Sparse and Compressive Sensing Techniques (2 papers) and Advanced Mathematical Modeling in Engineering (2 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (57 citations), Statistics and Probability (48 citations), Statistical and Nonlinear Physics (66 citations), Computational Mathematics (2 citations) and Numerical Analysis (11 citations). Jonas Latz has collaborated with scholars based in United Kingdom, Germany and Switzerland. Frequent co-authors include Elisabeth Ullmann, Carola‐Bibiane Schönlieb, Iason Papaioannou, Fabian Wagner, Kei Fong Lam, Raúl Tempone, Tobias Neckel, Fabio Nobile, Claudia Schillings and Dániel Straub. Their work appears in journals such as Inverse Problems, SIAM Journal on Scientific Computing, SIAM/ASA Journal on Uncertainty Quantification, Statistics and Computing and SIAM Review.
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.