Jakob Zech
- Statistical and Nonlinear Physics top 5%
- Statistics, Probability and Uncertainty top 2%
- Computational Mechanics top 10%
- Artificial Intelligence
- Computational Theory and Mathematics top 10%
- Co-authors
- Christoph SchwabCh. SchwabCarlos Jerez-HanckesAlbert CohenYoussef MarzoukÐinh DũngStefan Sauter
- Topics
- Probabilistic and Robust Engineering Design (10 papers)Model Reduction and Neural Networks (7 papers)Advanced Numerical Methods in Computational Mathematics (6 papers)
- Partner nations
- SwitzerlandGermanyUnited States
In The Last Decade
Jakob Zech
20 papers receiving 288 citations
Peers
Comparison fields: 5 of 36
- Statistical and Nonlinear Physics 130
- Statistics, Probability and Uncertainty 98
- Computational Mechanics 94
- Artificial Intelligence 81
- Computational Theory and Mathematics 66
Countries citing papers authored by Jakob Zech
This map shows the geographic impact of Jakob Zech'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 Jakob Zech with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jakob Zech more than expected).
Fields of papers citing papers by Jakob Zech
This network shows the impact of papers produced by Jakob Zech. 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 Jakob Zech. The network helps show where Jakob Zech may publish in the future.
Co-authorship network of co-authors of Jakob Zech
This figure shows the co-authorship network connecting the top 25 collaborators of Jakob Zech. A scholar is included among the top collaborators of Jakob Zech 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 Jakob Zech. Jakob Zech is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 3 | |
| 3 | 5 | |
| 4 | 3 | |
| 5 | 8 | |
| 6 | 1 | |
| 7 | 8 | |
| 8 | 9 | |
| 9 | 46 | |
| 10 | Deep ReLU Neural Network Expression Rates for Data-to-QoI Maps in Bayesian PDE Inversion | 2 |
| 11 | Deep learning in high dimension: ReLU network Expression Rates for Bayesian PDE inversion | 4 |
| 12 | 2 | |
| 13 | 21 | |
| 14 | 20 | |
| 15 | 1 | |
| 16 | 16 | |
| 17 | 85 | |
| 18 | 26 | |
| 19 | 33 | |
| 20 | 10 |
About Jakob Zech
Jakob Zech is a scholar working on Statistics, Probability and Uncertainty, Numerical Analysis and Statistical and Nonlinear Physics, having authored 20 papers that have together received 304 indexed citations. Recurring topics across this work include Probabilistic and Robust Engineering Design (10 papers), Model Reduction and Neural Networks (7 papers) and Advanced Numerical Methods in Computational Mathematics (6 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (98 citations), Statistical and Nonlinear Physics (130 citations) and Numerical Analysis (55 citations). Jakob Zech has collaborated with scholars based in Switzerland, Germany and United States. Frequent co-authors include Christoph Schwab, Ch. Schwab, Carlos Jerez-Hanckes, Albert Cohen, Youssef Marzouk, Ðinh Dũng and Stefan Sauter. Their work appears in journals such as Neural Networks, SIAM Journal on Numerical Analysis and Lecture notes in mathematics.
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.