World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
5638
World Ranking
12537
National Ranking
5088

Chao Hu publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Chao Hu sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 115 publications — 13th percentile

13% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Chao Hu D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Chao Hu sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Chao Hu is affiliated with Iowa State University in the United States and specializes in engineering with a focus on electrical and electronic engineering, control and systems engineering, automotive engineering, mechanical engineering, and artificial intelligence. Their research contributions span numerous interdisciplinary areas within engineering, particularly emphasizing advanced battery technologies and machine fault diagnosis.

Their publication record includes extensive research in the following key topics:

  • Advanced Battery Technologies Research
  • Advancements in Battery Materials
  • Fault Detection and Control Systems
  • Machine Fault Diagnosis Techniques
  • Reliability and Maintenance Optimization
  • Probabilistic and Robust Engineering Design
  • Structural Health Monitoring Techniques

Chao Hu has collaborated frequently with several researchers, including:

  • Adam Thelen
  • Simon Laflamme
  • Venkat Pavan Nemani
  • Vahid Barzegar
  • Zhen Hu

The research output is regularly published in several venues, with multiple papers appearing in:

  • Structural and Multidisciplinary Optimization
  • Mechanical Systems and Signal Processing
  • ECS Meeting Abstracts
  • Journal of Power Sources
  • arXiv (Cornell University)

Among their recent papers, notable works include:

  • "A comprehensive review of digital twin - part 1: modeling and twinning enabling technologies" (2022), published in Structural and Multidisciplinary Optimization
  • "A physics-informed deep learning approach for bearing fault detection" (2021), published in Engineering Applications of Artificial Intelligence
  • "Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial" (2023), published in Mechanical Systems and Signal Processing
  • "Physics-based prognostics of implantable-grade lithium-ion battery for remaining useful life prediction" (2020), published in Journal of Power Sources
  • "A comprehensive review of digital twin-part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives" (2022), published in Structural and Multidisciplinary Optimization

Their work reflects integration across computational approaches and experimental methods, particularly in the context of battery system reliability, fault detection, and structural health monitoring. The recurring themes in their publications include digital twin technology, machine learning-based prognostics, and probabilistic modeling related to engineering design and maintenance.

Best Publications

  • A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation

    Chao Hu;Byeng D. Youn;Jaesik Chung

  • Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries

    Sheng Shen;Mohammadkazem Sadoughi;Meng Li;Zhengdao Wang

  • Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life

    Chao Hu;Byeng D. Youn;Pingfeng Wang

  • A deep learning method for online capacity estimation of lithium-ion batteries

    Sheng Shen;Mohammadkazem Sadoughi;Xiangyi Chen;Mingyi Hong

  • Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery

    Chao Hu;Gaurav Jain;Puqiang Zhang;Craig Schmidt

  • Resilience-Driven System Design of Complex Engineered Systems

    Byeng D. Youn;Chao Hu;Pingfeng Wang

  • A physics-informed deep learning approach for bearing fault detection

    Sheng Shen;Hao Lu;Mohammadkazem Sadoughi;Chao Hu

  • Adaptive-sparse polynomial chaos expansion for reliability analysis and design of complex engineering systems

    Chao Hu;Byeng D. Youn

  • A generic probabilistic framework for structural health prognostics and uncertainty management

    Pingfeng Wang;Byeng D. Youn;Chao Hu

  • Online Estimation of Lithium-Ion Battery Capacity Using Sparse Bayesian Learning

    Chao Hu;Gaurav Jain;Craig Schmidt;Carrie Strief

  • Physics-Based Convolutional Neural Network for Fault Diagnosis of Rolling Element Bearings

    Mohammadkazem Sadoughi;Chao Hu

  • Multi-dimensional variational mode decomposition for bearing-crack detection in wind turbines with large driving-speed variations

    Zhixiong Li;Zhixiong Li;Yu Jiang;Yu Jiang;Qiang Guo;Chao Hu

  • An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction

    Zhixiong Li;Dazhong Wu;Chao Hu;Janis P. Terpenny

  • A generic model-free approach for lithium-ion battery health management

    Guangxing Bai;Pingfeng Wang;Chao Hu;Michael Pecht

  • Uses, Cost-Benefit Analysis, and Markets of Energy Storage Systems for Electric Grid Applications

    Jinqiang Liu;Chao Hu;Anne Kimber;Zhaoyu Wang

  • Remaining useful life assessment of lithium-ion batteries in implantable medical devices

    Chao Hu;Hui Ye;Gaurav Jain;Craig Schmidt

  • A Generalized Complementary Intersection Method (GCIM) for System Reliability Analysis

    Pingfeng Wang;Chao Hu;Byeng D. Youn

  • A copula-based sampling method for data-driven prognostics

    Zhimin Xi;Rong Jing;Pingfeng Wang;Chao Hu

  • Optimal sensor placement within a hybrid dense sensor network using an adaptive genetic algorithm with learning gene pool

    Austin Downey;Chao Hu;Simon Laflamme

  • An optimized ensemble local mean decomposition method for fault detection of mechanical components

    Chao Zhang;Zhixiong Li;Zhixiong Li;Chao Hu;Shuai Chen

  • A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation

    Chao Hu;Byeng Dong Youn;Jae Sik Chung;Randy Ortanez

Frequent Co-Authors

Byeng D. Youn
Byeng D. Youn Seoul National University
Simon Laflamme
Simon Laflamme Iowa State University
Zhongxiao Peng
Zhongxiao Peng University of New South Wales
Zhaoyu Wang
Zhaoyu Wang Iowa State University
Mingyi Hong
Mingyi Hong University of Minnesota
Dazhong Wu
Dazhong Wu University of Central Florida
Sankaran Mahadevan
Sankaran Mahadevan Vanderbilt University
Halil Ceylan
Halil Ceylan Iowa State University
Michael Pecht
Michael Pecht University of Maryland, College Park
Hyun Jae Kim
Hyun Jae Kim Yonsei University

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