World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
46
Citations
15135
World Ranking
1319
National Ranking
24

Engineering and Technology

D-Index
46
Citations
15181
World Ranking
5036
National Ranking
77

Fabio Nobile publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Fabio Nobile sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 226 publications — 71st percentile

71% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Fabio Nobile D-index placement in Mathematics in 2026

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

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 46 D-Index — 64th percentile

64% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

Overview

Fabio Nobile is affiliated with the École Polytechnique Fédérale de Lausanne in Switzerland. Their research spans multiple fields centered on engineering and decision sciences with a specific focus on statistics, probability, and uncertainty as well as computational mechanics and numerical analysis.

Their recent scholarly contributions have appeared in various scientific venues. Notable publications include:

  • Quantifying uncertain system outputs via the multilevel Monte Carlo method - Part I: Central moment estimation, 2020, Journal of Computational Physics
  • Complexity Analysis of stochastic gradient methods for PDE-constrained optimal Control Problems with uncertain parameters, 2021, ESAIM Mathematical Modelling and Numerical Analysis
  • Sparse Polynomial Chaos expansions using variational relevance vector machines, 2020, Journal of Computational Physics
  • Integration of activation maps of epicardial veins in computational cardiac electrophysiology, 2020, Computers in Biology and Medicine
  • Some first results on the consistency of spatial regression with partial differential equation regularization, 2020, Statistica Sinica

Fabio Nobile frequently collaborates with a core group of co-authors who have contributed to several joint works, including Tommaso Vanzan, Yoshihito Kazashi, Raúl Tempone, Davide Pradovera, and Thomas Trigo Trindade.

Their publications are regularly featured in multiple established outlets such as:

  • arXiv (Cornell University)
  • Infoscience (École Polytechnique Fédérale de Lausanne)
  • Zenodo (CERN European Organization for Nuclear Research)
  • SIAM/ASA Journal on Uncertainty Quantification
  • Numerische Mathematik

The scientist's research interests are concentrated around several main topics, including:

  • Probabilistic and Robust Engineering Design
  • Model Reduction and Neural Networks
  • Advanced Numerical Methods in Computational Mathematics
  • Mathematical Approximation and Integration
  • Advanced Mathematical Modeling in Engineering
  • Sparse and Compressive Sensing Techniques
  • Image and Signal Denoising Methods

Their work blends theoretical development and computational applications, often addressing uncertainty quantification, advanced statistical techniques, and the computational challenges in engineering and applied mathematics.

Best Publications

  • A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

    Ivo Babus caron;ka;Fabio Nobile;Rau´l Tempone

  • A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

    Ivo Babuška;Fabio Nobile;Raúl Tempone

  • A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data

    F. Nobile;R. Tempone;C. G. Webster

  • Added-mass effect in the design of partitioned algorithms for fluid-structure problems

    Paola Causin;Jean-Frédéric Gerbeau;Fabio Nobile

  • On the coupling of 3D and 1D Navier-Stokes equations for flow problems in compliant vessels

    Luca Formaggia;Jean Frédéric Gerbeau;Fabio Nobile;Alfio Quarteroni;Alfio Quarteroni

  • An Anisotropic Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data

    F. Nobile;R. Tempone;C. G. Webster

  • Numerical approximation of fluid-structure interaction problems with application to haemodynamics

    Fabio Nobile

  • Multiscale Modelling of the Circulatory System: a Preliminary Analysis

    Luca Formaggia;Fabio Nobile;Alfio Quarteroni;Alessandro Veneziani

  • Numerical Treatment of Defective Boundary Conditions for the Navier--Stokes Equations

    L. Formaggia;J.-F. Gerbeau;F. Nobile;A. Quarteroni

  • Fluid-structure partitioned procedures based on Robin transmission conditions

    Santiago Badia;Fabio Nobile;Christian Vergara

  • A Stability Analysis for the Arbitrary Lagrangian Eulerian Formulation with Finite Elements

    Luca Formaggia;Fabio Nobile

  • An Effective Fluid-Structure Interaction Formulation for Vascular Dynamics by Generalized Robin Conditions

    F. Nobile;C. Vergara

  • Stochastic Spectral Galerkin and Collocation Methods for PDEs with Random Coefficients: A Numerical Comparison

    Joakim Bäck;Fabio Nobile;Lorenzo Tamellini;Raul Tempone

  • Stability analysis of second-order time accurate schemes for ALE-FEM

    Luca Formaggia;Fabio Nobile

  • Multi-index Monte Carlo: when sparsity meets sampling

    Abdul-Lateef Haji-Ali;Fabio Nobile;Raúl Tempone

  • ON THE OPTIMAL POLYNOMIAL APPROXIMATION OF STOCHASTIC PDES BY GALERKIN AND COLLOCATION METHODS

    Joakim Beck;Raul Tempone;Fabio Nobile;Fabio Nobile;Lorenzo Tamellini

  • Robin-Robin preconditioned Krylov methods for fluid-structure interaction problems

    Santiago Badia;Fabio Nobile;Christian Vergara

  • A continuation multilevel Monte Carlo algorithm

    Nathan Collier;Abdul-Lateef Haji-Ali;Fabio Nobile;Erik von Schwerin

  • Discrete least squares polynomial approximation with random evaluations − application to parametric and stochastic elliptic PDEs

    Abdellah Chkifa;Albert Cohen;Giovanni Migliorati;Fabio Nobile

  • Analysis and implementation issues for the numerical approximation of parabolic equations with random coefficients

    Fabio Nobile;Raul Tempone;Raul Tempone

  • On uncertainty quantification in hydrogeology and hydrogeophysics

    Niklas Linde;David Ginsbourger;David Ginsbourger;James Irving;Fabio Nobile

Frequent Co-Authors

Raul Tempone
Raul Tempone King Abdullah University of Science and Technology
Alfio Quarteroni
Alfio Quarteroni Polytechnic University of Milan
Luca Formaggia
Luca Formaggia Polytechnic University of Milan
Ivo Babuška
Ivo Babuška The University of Texas at Austin
Daniel Kressner
Daniel Kressner École Polytechnique Fédérale de Lausanne
Alessandro Veneziani
Alessandro Veneziani Emory University
Ilaria Perugia
Ilaria Perugia University of Vienna
J. T. Oden
J. T. Oden The University of Texas at Austin
Jean-Frédéric Gerbeau
Jean-Frédéric Gerbeau French Institute for Research in Computer Science and Automation - INRIA
James C. Browne
James C. Browne The University of Texas at Austin

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students studying Mathematics in the USA, exploring related online degrees can widen career opportunities and enhance skill sets. Many graduates pursue advanced management roles through programs like the best 1 year mba programs, which offer accelerated paths to leadership positions without a long time commitment.

Online MBA programs often provide flexible options for credit transfers, making it easier for students to leverage previous coursework. Those interested should consider mba programs that accept transfer credits, as these can reduce the overall time and cost required to complete the degree.

Additionally, the growing demand for data-driven decision making has made data analytics masters programs highly relevant. These programs complement mathematical expertise and open doors to careers in big data, business intelligence, and quantitative analysis.

For those seeking a smoother application process, exploring the easiest mba options can be a pragmatic starting point. Such programs often have more accessible entry criteria while still providing valuable credentials and networking opportunities.

Best Scientists Citing Fabio Nobile

Trending Scientists

Recently Published Articles