Anh Tran

1.2k citations
35 papers · 457 indexed · 1 hit paper · h-index 12

Anh Tran

31 papers receiving 447 citations

Hit Papers

Uncertainty quantification in machine learning for engine...1032023202620242025255075100

Peers

Anh Tran
Comparison fields: 5 of 80
  • Statistics, Probability and Uncertainty 96
  • Computational Theory and Mathematics 112
  • Management Science and Operations Research 56
  • Ecological Modeling 14
  • Industrial and Manufacturing Engineering 31
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Guofa Li China
Zhongmei Gao China
Young-Jin Kang South Korea
Jinju Sun China
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Anh Tran relative to Guofa Li China Guofa Li's profile →
Citations per field
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Guofa Li · 1×
Citations per year

Countries citing papers authored by Anh Tran

Since Specialization
Citations

This map shows the geographic impact of Anh Tran'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 Anh Tran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anh Tran more than expected).

Fields of papers citing papers by Anh Tran

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anh Tran. 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 Anh Tran. The network helps show where Anh Tran may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Anh Tran, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Anh Tran Line = papers co-authored together Anh Tran links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20252
2 20251
3 20250
4 20240
5 20232
6 202310
7 20230
8 20231
9
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorialbreakdown →
2023103
10 20222
11 20220
12 20217
13 20214
14
Multiscale stochastic reduced-order model for uncertainty propagation using Fokker-Planck equation with microstructure evolution applications.
20201
15 201911
16 201923
17 201948
18 20199
19 201712
20 201614

About Anh Tran

Anh Tran is a scholar working on Statistics, Probability and Uncertainty, Computational Theory and Mathematics and Statistical and Nonlinear Physics, having authored 35 papers that have together received 457 indexed citations. Recurring topics across this work include Advanced Multi-Objective Optimization Algorithms (11 papers), Probabilistic and Robust Engineering Design (11 papers), Gaussian Processes and Bayesian Inference (5 papers), Model Reduction and Neural Networks (4 papers), Microstructure and mechanical properties (4 papers), Metallurgy and Material Forming (4 papers), Optimal Experimental Design Methods (4 papers) and Machine Learning in Materials Science (3 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (96 citations), Computational Theory and Mathematics (112 citations) and Management Science and Operations Research (56 citations). Anh Tran has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Yan Wang, Timothy Wildey, Scott McCann, Robert Visintainer, Krishnan V. Pagalthivarthi, Hojun Lim, Hoang Tran, Minh–Triet Tran, Olga Fink and Xiaoge Zhang. Their work appears in journals such as Acta Materialia, Computer Methods in Applied Mechanics and Engineering and Mechanical Systems and Signal Processing.

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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