Jack Xin
- Numerical Analysis top 1%
- Modeling and Simulation top 1%
- Mathematical Biology Tumor Growth 12
- Computational Mechanics top 0.5%
- Sparse and Compressive Sensing Techniques 28
- Fluid Dynamics and Turbulent Flows 15
- Mathematical Physics top 2%
- Stochastic processes and statistical mechanics 12
- Applied Mathematics top 2%
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- Blind Source Separation Techniques 20
- Speech and Audio Processing 19
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- Advanced Mathematical Modeling in Engineering 20
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- Nonlinear Dynamics and Pattern Formation 12
- Journals
- Communications in Mathematical Sciences (11 papers)Physica D Nonlinear Phenomena (8 papers)Communications in Mathematical Physics (7 papers)
- Partner nations
- United StatesChinaHong Kong
In The Last Decade
Jack Xin
146 papers receiving 2.8k citations
Peers
Comparison fields: 5 of 114
- Numerical Analysis 490
- Modeling and Simulation 321
- Computational Mechanics 1.1k
- Mathematical Physics 414
- Applied Mathematics 373
Countries citing papers authored by Jack Xin
This map shows the geographic impact of Jack Xin'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 Jack Xin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jack Xin more than expected).
Fields of papers citing papers by Jack Xin
This network shows the impact of papers produced by Jack Xin. 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 Jack Xin. The network helps show where Jack Xin may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jack Xin, 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 | 2025 | 0 | |
| 2 | 2024 | 4 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 2 | |
| 5 | 2023 | 1 | |
| 6 | 2022 | 1 | |
| 7 | 2022 | 0 | |
| 8 | 2021 | 5 | |
| 9 | 2021 | 10 | |
| 10 | Sharp uniform in time error estimate on a stochastic structure-preserving Lagrangian method and computation of effective diffusivity in 3D chaotic flows | 2018 | 1 |
| 11 | 2017 | 44 | |
| 12 | Training Ternary Neural Networks with Exact Proximal Operator. | 2016 | 1 |
| 13 | 2012 | 111 | |
| 14 | 2012 | 6 | |
| 15 | 2010 | 40 | |
| 16 | 2006 | 2 | |
| 17 | 2003 | 25 | |
| 18 | 1997 | 5 | |
| 19 | Global existence and large time asymptotic bounds of $L^\infty $ solutions of thermal diffusive combustion systems on $\mathbb {R}^n$ | 1996 | 16 |
| 20 | 1996 | 33 |
About Jack Xin
Jack Xin is a scholar working on Mathematical Physics, Computational Mechanics and Modeling and Simulation, having authored 162 papers that have together received 3.1k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (28 papers), Blind Source Separation Techniques (20 papers), Advanced Mathematical Modeling in Engineering (20 papers), Speech and Audio Processing (19 papers), Fluid Dynamics and Turbulent Flows (15 papers), Nonlinear Dynamics and Pattern Formation (12 papers), Mathematical Biology Tumor Growth (12 papers) and Stochastic processes and statistical mechanics (12 papers). The work is most often cited by research in Numerical Analysis (490 citations), Modeling and Simulation (321 citations) and Computational Mechanics (1.1k citations). Jack Xin has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Yifei Lou, Penghang Yin, James Nolen, Qi He, Stanley Osher, Ernie Esser, Yingyong Qi, Yifeng Yu, Tieyong Zeng and Fen Lin. Their work appears in journals such as Communications in Mathematical Sciences, Physica D Nonlinear Phenomena, Communications in Mathematical Physics, Journal of Scientific Computing and Nonlinearity.
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.