World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
37
Citations
6913
World Ranking
2465
National Ranking
153

Engineering and Technology

D-Index
37
Citations
7088
World Ranking
8287
National Ranking
278

Overview

Hermann G. Matthies is affiliated with Technische Universität Braunschweig in Germany. Their research spans multiple areas within engineering and decision sciences, with a focus on uncertainty quantification, computational mechanics, and material modeling. The primary fields of study include Engineering and Decision Sciences, supported by substantial work in subfields such as Statistics, Probability and Uncertainty, Mechanics of Materials, Civil and Structural Engineering, Computational Mathematics, and Statistical and Nonlinear Physics.

The scientist's work covers a wide range of topics, notably:

  • Probabilistic and Robust Engineering Design
  • Composite Material Mechanics
  • Tensor decomposition and applications
  • Numerical methods in engineering
  • Structural Health Monitoring Techniques
  • Rock Mechanics and Modeling
  • Model Reduction and Neural Networks

Matthies has contributed to prominent journals and publication venues, including:

  • Computer Methods in Applied Mechanics and Engineering
  • arXiv (Cornell University)
  • Metals
  • Journal of Computational Physics
  • Advanced Modeling and Simulation in Engineering Sciences

Their recent papers illustrate a focus on probabilistic and computational approaches to material and fracture modeling, including:

  • "A modified parallelepiped model for non-probabilistic uncertainty quantification and propagation analysis," 2020, Computer Methods in Applied Mechanics and Engineering
  • "Stochastic phase-field modeling of brittle fracture: computing multiple crack patterns and their probabilities," 2020, arXiv (Cornell University)
  • "Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage," 2020, Metals
  • "Parameter identification for phase-field modeling of fracture: a Bayesian approach with sampling-free update," 2020, Computational Mechanics
  • "FFT-based homogenisation accelerated by low-rank tensor approximations," 2020, Computer Methods in Applied Mechanics and Engineering

Frequent collaborations have been observed with researchers such as Bojana Rosić, Adnan Ibrahimbegović, Jaroslav Vondřejc, Laura De Lorenzis, and Sharana Kumar Shivanand. These collaborations further emphasize the interdisciplinary nature of Matthies's research activities.

Best Publications

  • The solution of nonlinear finite element equations

    Hermann Matthies;Gilbert Strang

  • Galerkin methods for linear and nonlinear elliptic stochastic partial differential equations

    Hermann G. Matthies;Andreas Keese

  • Uncertainties in probabilistic numerical analysis of structures and solids-Stochastic finite elements

    Hermann G. Matthies;Christoph E. Brenner;Christian G. Bucher;C. Guedes Soares

  • Partitioned Strong Coupling Algorithms for Fluid-Structure-Interaction

    Hermann G. Matthies;Jan Steindorf

  • Algorithms for strong coupling procedures

    Hermann G. Matthies;Rainer Niekamp;Jan Steindorf

  • Efficient model reduction in non-linear dynamics using the Karhunen-Loève expansion and dual-weighted-residual methods

    Marcus Meyer;Hermann G. Matthies

  • Partitioned but strongly coupled iteration schemes for nonlinear fluid-structure interaction

    Hermann G. Matthies;Jan Steindorf

  • Finite elements for stochastic media problems

    Hermann G. Matthies;Christian Bucher

  • Parameter identification in a probabilistic setting

    Bojana V. Rosić;Bojana V. Rosić;Anna Kučerová;Jan Sýkora;Oliver Pajonk

  • Application of hierarchical matrices for computing the Karhunen–Loève expansion

    B. N. Khoromskij;A. Litvinenko;H. G. Matthies

  • Nonlinear fluid---structure interaction problem. Part I: implicit partitioned algorithm, nonlinear stability proof and validation examples

    Christophe Kassiotis;Adnan Ibrahimbegovic;Rainer Niekamp;Hermann G. Matthies

  • Stochastic finite elements: Computational approaches to stochastic partial differential equations

    Hermann G. Matthies

  • Hierarchical parallelisation for the solution of stochastic finite element equations

    Andreas Keese;Hermann G. Matthies

  • Uncertainty Quantification with Stochastic Finite Elements

    Hermann G. Matthies

  • QUANTIFYING UNCERTAINTY: MODERN COMPUTATIONAL REPRESENTATION OF PROBABILITY AND APPLICATIONS

    Hermann G. Matthies

  • Solving stochastic systems with low-rank tensor compression

    Hermann G. Matthies;Elmar Zander

  • Sampling-free linear Bayesian update of polynomial chaos representations

    Bojana V. Rosić;Alexander Litvinenko;Oliver Pajonk;Hermann G. Matthies

  • Efficient low-rank approximation of the stochastic Galerkin matrix in tensor formats

    Mike Espig;Wolfgang Hackbusch;Alexander Litvinenko;Hermann G. Matthies

  • A deterministic filter for non-Gaussian Bayesian estimation— Applications to dynamical system estimation with noisy measurements

    Oliver Pajonk;Bojana V. Rosić;Alexander Litvinenko;Hermann G. Matthies

  • Efficient Analysis of High Dimensional Data in Tensor Formats

    Mike Espig;Wolfgang Hackbusch;Alexander Litvinenko;Hermann G. Matthies

Frequent Co-Authors

Adnan Ibrahimbegovic
Adnan Ibrahimbegovic University of Technology of Compiègne
Boris N. Khoromskij
Boris N. Khoromskij Max Planck Institute for Mathematics in the Sciences
Roger Ohayon
Roger Ohayon Conservatoire National des Arts et Métiers
Wolfgang Hackbusch
Wolfgang Hackbusch Max Planck Institute for Mathematics in the Sciences
David E. Keyes
David E. Keyes King Abdullah University of Science and Technology
C. Guedes Soares
C. Guedes Soares Instituto Superior Técnico
Ali Kaveh
Ali Kaveh Iran University of Science and Technology
Giuseppe Gambolati
Giuseppe Gambolati University of Padua

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