World's Best Scientists 2026 revealed!
Bruno Sudret

Bruno Sudret

D-Index & Metrics

Engineering and Technology

D-Index
55
Citations
17720
World Ranking
2926
National Ranking
48

Bruno Sudret publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Bruno Sudret sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 350 publications — 84th percentile

84% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Bruno Sudret D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Bruno Sudret sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 55 D-Index — 70th percentile

70% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Bruno Sudret is affiliated with ETH Zurich in Switzerland. Their research spans multiple fields with a primary focus on decision sciences and engineering, contributing substantially to domains such as statistics, probability and uncertainty, computational theory and mathematics, civil and structural engineering, management science and operations research, and control and systems engineering.

The main topics of Bruno Sudret's work include:

  • Probabilistic and Robust Engineering Design
  • Advanced Multi-Objective Optimization Algorithms
  • Simulation Techniques and Applications
  • Model Reduction and Neural Networks
  • Structural Health Monitoring Techniques
  • Fault Detection and Control Systems
  • Structural Response to Dynamic Loads

Bruno Sudret has published extensively, with notable recent papers including:

  • "Active learning for structural reliability: Survey, general framework and benchmark," 2022, Structural Safety
  • "EXTENDING CLASSICAL SURROGATE MODELING TO HIGH DIMENSIONS THROUGH SUPERVISED DIMENSIONALITY REDUCTION: A DATA-DRIVEN APPROACH," 2020, International Journal for Uncertainty Quantification
  • "What is the optimal robust environmental and cost-effective solution for building renovation? Not the usual one," 2021, Energy and Buildings
  • "Bio-based materials as a robust solution for building renovation: A case study," 2022, Applied Energy
  • "A global sensitivity analysis framework for hybrid simulation," 2020, Mechanical Systems and Signal Processing

The scientist frequently publishes in venues such as:

  • arXiv (Cornell University)
  • Repository for Publications and Research Data (ETH Zurich)
  • International Journal for Uncertainty Quantification
  • Reliability Engineering & System Safety
  • HAL (Le Centre pour la Communication Scientifique Directe)

Collaborations form a significant aspect of Bruno Sudret's scholarly activity. Frequent co-authors include:

  • Stefano Marelli
  • Maliki Moustapha
  • Xujia Zhu
  • Alina Galimshina
  • Guillaume Habert

Best Publications

  • Global sensitivity analysis using polynomial chaos expansions

    Bruno Sudret

  • Adaptive sparse polynomial chaos expansion based on least angle regression

    Géraud Blatman;Bruno Sudret

  • An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis

    Géraud Blatman;Bruno Sudret

  • UQLab: a framework for Uncertainty Quantification in MATLAB

    Stefano Marelli;Bruno Sudret

  • Metamodel-based importance sampling for structural reliability analysis

    V. Dubourg;Bruno Sudret;F. Deheeger

  • Stochastic finite element: a non intrusive approach by regression

    Marc Berveiller;Bruno Sudret;Maurice Lemaire

  • The PHI2 method: a way to compute time-variant reliability

    C. Andrieu-Renaud;Bruno Sudret;Maurice Lemaire

  • Efficient computation of global sensitivity indices using sparse polynomial chaos expansions

    Géraud Blatman;Bruno Sudret

  • Polynomial-Chaos-based Kriging

    Roland Schobi;Bruno Sudret;Joe Wiart

  • Reliability-based design optimization using kriging surrogates and subset simulation

    Vincent Dubourg;Bruno Sudret;Jean-Marc Bourinet

  • Comparison of finite element reliability methods

    Bruno Sudret;Armen Der Kiureghian

  • Analytical derivation of the outcrossing rate in time-variant reliability problems

    Bruno Sudret

  • Sparse polynomial chaos expansions and adaptive stochastic finite elements using a regression approach

    Géraud Blatman;Bruno Sudret

  • Rare Event Estimation Using Polynomial-Chaos Kriging

    R. Schöbi;Bruno Sudret;Stefano Marelli

  • Active learning for structural reliability: Survey, general framework and benchmark

    Unknown

  • An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis

    Stefano Marelli;Bruno Sudret

  • A new surrogate modeling technique combining Kriging and polynomial chaos expansions - Application to uncertainty analysis in computational dosimetry

    Pierric Kersaudy;Bruno Sudret;Nadège Varsier;Odile Picon

  • Meta-model-based importance sampling for reliability sensitivity analysis

    V. Dubourg;Bruno Sudret

  • Euclid preparation: II. The EuclidEmulator – a tool to compute the cosmology dependence of the nonlinear matter power spectrum

    Mischa Knabenhans;Joachim Stadel;Stefano Marelli;Doug Potter

  • Surrogate-assisted reliability-based design optimization: a survey and a unified modular framework

    Maliki Moustapha;Bruno Sudret

  • Metamodel-based sensitivity analysis: polynomial chaos expansions and Gaussian processes

    Loic Le Gratiet;Stefano Marelli;Bruno Sudret

  • Metamodel-based importance sampling for structural reliability analysis

    Vincent Dubourg;François Deheeger;Bruno Sudret

Frequent Co-Authors

Enrico Zio
Enrico Zio Polytechnic University of Milan
Joe Wiart
Joe Wiart Institut Mines-Télécom
Paul Embrechts
Paul Embrechts ETH Zurich
Eleni Chatzi
Eleni Chatzi ETH Zurich
Anis Younes
Anis Younes University of Strasbourg
Andrea Stenke
Andrea Stenke ETH Zurich
Thomas Peter
Thomas Peter ETH Zurich
Irena Hajnsek
Irena Hajnsek German Aerospace Center

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