Jakob Zech

620 total citations
20 papers, 304 citations indexed

About

Jakob Zech is a scholar working on Statistics, Probability and Uncertainty, Statistical and Nonlinear Physics and Computational Mechanics. According to data from OpenAlex, Jakob Zech has authored 20 papers receiving a total of 304 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Statistics, Probability and Uncertainty, 7 papers in Statistical and Nonlinear Physics and 6 papers in Computational Mechanics. Recurrent topics in Jakob Zech's 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). Jakob Zech is often cited by papers focused on Probabilistic and Robust Engineering Design (10 papers), Model Reduction and Neural Networks (7 papers) and Advanced Numerical Methods in Computational Mathematics (6 papers). Jakob Zech collaborates with scholars based in Switzerland, Germany and United States. Jakob Zech's co-authors include Christoph Schwab, Ch. Schwab, Carlos Jerez-Hanckes, Albert Cohen, Youssef Marzouk, Ðinh Dũng and Stefan Sauter and has published in prestigious journals such as Neural Networks, SIAM Journal on Numerical Analysis and Lecture notes in mathematics.

In The Last Decade

Jakob Zech

20 papers receiving 288 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jakob Zech Switzerland	9	130	98	94	81	66	20	304
Tong Qin United States	7	203 1.6×	82 0.8×	165 1.8×	68 0.8×	48 0.7×	11	387
Abdellah Chkifa France	6	89 0.7×	264 2.7×	160 1.7×	29 0.4×	112 1.7×	16	400
Mamikon Gulian United States	7	111 0.9×	26 0.3×	65 0.7×	26 0.3×	71 1.1×	16	410
Denis Ridzal United States	11	47 0.4×	96 1.0×	197 2.1×	24 0.3×	141 2.1×	28	388
Jonas Ballani Germany	8	123 0.9×	56 0.6×	97 1.0×	25 0.3×	135 2.0×	12	343
Alexey Chernov Germany	11	29 0.2×	105 1.1×	91 1.0×	40 0.5×	129 2.0×	40	322
Leonardo Robol Italy	12	61 0.5×	46 0.5×	37 0.4×	22 0.3×	193 2.9×	46	419
Patrick Blonigan United States	10	235 1.8×	78 0.8×	168 1.8×	26 0.3×	28 0.4×	38	385
Ninoslav Truhar Croatia	11	178 1.4×	71 0.7×	69 0.7×	11 0.1×	252 3.8×	39	422
Michael Peters Switzerland	10	15 0.1×	127 1.3×	90 1.0×	24 0.3×	88 1.3×	19	326

Countries citing papers authored by Jakob Zech

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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

20 of 20 papers shown

Zech, Jakob, et al.. (2025). Metropolis-adjusted interacting particle sampling. Statistics and Computing. 35(3). 1 indexed citations

Zech, Jakob, et al.. (2024). Neural and spectral operator surrogates: unified construction and expression rate bounds. Advances in Computational Mathematics. 50(4). 3 indexed citations

Schwab, Christoph, et al.. (2023). De Rham compatible Deep Neural Network FEM. Neural Networks. 165. 721–739. 5 indexed citations

Dũng, Ðinh, et al.. (2023). Analyticity and Sparsity in Uncertainty Quantification for PDEs with Gaussian Random Field Inputs. Lecture notes in mathematics. 3 indexed citations

Schwab, Christoph & Jakob Zech. (2023). Deep Learning in High Dimension: Neural Network Expression Rates for Analytic Functions in $\pmb{L^2(\mathbb{R}^d,\gamma_d)}$. SIAM/ASA Journal on Uncertainty Quantification. 11(1). 199–234. 8 indexed citations

Jerez-Hanckes, Carlos, et al.. (2023). Multilevel domain uncertainty quantification in computational electromagnetics. Mathematical Models and Methods in Applied Sciences. 33(4). 877–921. 1 indexed citations

Zech, Jakob & Youssef Marzouk. (2022). Sparse Approximation of Triangular Transports, Part I: The Finite-Dimensional Case. Constructive Approximation. 55(3). 919–986. 8 indexed citations

Zech, Jakob & Youssef Marzouk. (2022). Sparse Approximation of Triangular Transports, Part II: The Infinite-Dimensional Case. Constructive Approximation. 55(3). 987–1036. 9 indexed citations

Schwab, Ch., et al.. (2021). Exponential ReLU DNN Expression of Holomorphic Maps in High Dimension. Constructive Approximation. 55(1). 537–582. 46 indexed citations

10.

Schwab, Christoph, et al.. (2020). Deep ReLU Neural Network Expression Rates for Data-to-QoI Maps in Bayesian PDE Inversion. 2 indexed citations

11.

Schwab, Christoph, et al.. (2020). Deep learning in high dimension: ReLU network Expression Rates for Bayesian PDE inversion. 4 indexed citations

12.

Jerez-Hanckes, Carlos, et al.. (2020). Domain Uncertainty Quantification in Computational Electromagnetics. SIAM/ASA Journal on Uncertainty Quantification. 8(1). 301–341. 2 indexed citations

13.

Zech, Jakob & Christoph Schwab. (2020). Convergence rates of high dimensional Smolyak quadrature. ESAIM Mathematical Modelling and Numerical Analysis. 54(4). 1259–1307. 21 indexed citations

14.

Schwab, Christoph, et al.. (2020). Deep neural network expression of posterior expectations in Bayesian PDE inversion. Inverse Problems. 36(12). 125011–125011. 20 indexed citations

15.

Schwab, Christoph, et al.. (2019). Uncertainty Quantification for Spectral Fractional Diffusion: Sparsity Analysis of Parametric Solutions. SIAM/ASA Journal on Uncertainty Quantification. 7(3). 913–947. 1 indexed citations

16.

Zech, Jakob, Ðinh Dũng, & Christoph Schwab. (2019). Multilevel approximation of parametric and stochastic PDES. Mathematical Models and Methods in Applied Sciences. 29(9). 1753–1817. 16 indexed citations

17.

Schwab, Christoph & Jakob Zech. (2018). Deep learning in high dimension: Neural network expression rates for generalized polynomial chaos expansions in UQ. Analysis and Applications. 17(1). 19–55. 85 indexed citations

18.

Cohen, Albert, Christoph Schwab, & Jakob Zech. (2018). Shape Holomorphy of the Stationary Navier--Stokes Equations. SIAM Journal on Mathematical Analysis. 50(2). 1720–1752. 26 indexed citations

19.

Jerez-Hanckes, Carlos, Christoph Schwab, & Jakob Zech. (2017). Electromagnetic wave scattering by random surfaces: Shape holomorphy. Mathematical Models and Methods in Applied Sciences. 27(12). 2229–2259. 33 indexed citations

20.

Sauter, Stefan & Jakob Zech. (2015). A Posteriori Error Estimation of $hp$-$dG$ Finite Element Methods for Highly Indefinite Helmholtz Problems. SIAM Journal on Numerical Analysis. 53(5). 2414–2440. 10 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.

Explore authors with similar magnitude of impact