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Michael Kaliske

Michael Kaliske

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
43
Citations
8324
World Ranking
1705
National Ranking
57

Michael Kaliske publication distribution in Mechanical and Aerospace Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mechanical and Aerospace Engineering in 2026. The highlighted bar marks where Michael Kaliske sits on this spectrum.

47–56 publications: 10 scientists 57–66 publications: 23 scientists 67–76 publications: 32 scientists 77–86 publications: 62 scientists 87–96 publications: 67 scientists 97–106 publications: 91 scientists 107–116 publications: 113 scientists 117–126 publications: 115 scientists 127–136 publications: 130 scientists 137–146 publications: 140 scientists 147–156 publications: 155 scientists 157–166 publications: 132 scientists 167–176 publications: 133 scientists 177–186 publications: 130 scientists 187–196 publications: 140 scientists 197–206 publications: 115 scientists 207–216 publications: 125 scientists 217–226 publications: 117 scientists 227–236 publications: 99 scientists 237–246 publications: 92 scientists 247–256 publications: 100 scientists 257–266 publications: 95 scientists 267–276 publications: 88 scientists 277–286 publications: 77 scientists 287–296 publications: 74 scientists 297–306 publications: 74 scientists 307–316 publications: 62 scientists 317–326 publications: 70 scientists 327–336 publications: 59 scientists 337–346 publications: 58 scientists 347–356 publications: 45 scientists 357–366 publications: 44 scientists 367–376 publications: 36 scientists 377–386 publications: 41 scientists 387–396 publications: 32 scientists 397–406 publications: 23 scientists 407–416 publications: 28 scientists 417–426 publications: 27 scientists 427–436 publications: 25 scientists 437–446 publications: 23 scientists 447–456 publications: 23 scientists 457–466 publications: 20 scientists 467–476 publications: 12 scientists 477–486 publications: 24 scientists 487–496 publications: 18 scientists 497–506 publications: 12 scientists 507–516 publications: 13 scientists 517–526 publications: 21 scientists 527–536 publications: 12 scientists 537–546 publications: 8 scientists 547–556 publications: 16 scientists 557–566 publications: 3 scientists 567–576 publications: 11 scientists 577–586 publications: 6 scientists 587–596 publications: 5 scientists 597–606 publications: 6 scientists 607–616 publications: 7 scientists 617–626 publications: 7 scientists 627–636 publications: 10 scientists 637–646 publications: 4 scientists 647–656 publications: 3 scientists 657–658 publications: 2 scientists 659+ publications: 100 scientists
47 publications 659+

This scientist: 487 publications — 92nd percentile

92% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 659 publications or more.

Michael Kaliske D-index placement in Mechanical and Aerospace Engineering in 2026

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

30 D-Index: 83 scientists 31 D-Index: 113 scientists 32 D-Index: 144 scientists 33 D-Index: 153 scientists 34 D-Index: 189 scientists 35 D-Index: 158 scientists 36 D-Index: 139 scientists 37 D-Index: 127 scientists 38 D-Index: 130 scientists 39 D-Index: 126 scientists 40 D-Index: 104 scientists 41 D-Index: 100 scientists 42 D-Index: 107 scientists 43 D-Index: 101 scientists 44 D-Index: 103 scientists 45 D-Index: 79 scientists 46 D-Index: 88 scientists 47 D-Index: 70 scientists 48 D-Index: 83 scientists 49 D-Index: 44 scientists 50 D-Index: 64 scientists 51 D-Index: 56 scientists 52 D-Index: 50 scientists 53 D-Index: 48 scientists 54 D-Index: 58 scientists 55 D-Index: 52 scientists 56 D-Index: 48 scientists 57 D-Index: 42 scientists 58 D-Index: 34 scientists 59 D-Index: 42 scientists 60 D-Index: 37 scientists 61 D-Index: 42 scientists 62 D-Index: 44 scientists 63 D-Index: 22 scientists 64 D-Index: 33 scientists 65 D-Index: 29 scientists 66 D-Index: 23 scientists 67 D-Index: 29 scientists 68 D-Index: 24 scientists 69 D-Index: 19 scientists 70 D-Index: 34 scientists 71 D-Index: 26 scientists 72 D-Index: 19 scientists 73 D-Index: 18 scientists 74 D-Index: 19 scientists 75 D-Index: 14 scientists 76 D-Index: 19 scientists 77 D-Index: 8 scientists 78 D-Index: 18 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 17 scientists 82 D-Index: 11 scientists 83 D-Index: 16 scientists 84 D-Index: 7 scientists 85 D-Index: 9 scientists 86 D-Index: 8 scientists 87 D-Index: 6 scientists 88 D-Index: 6 scientists 89 D-Index: 7 scientists 90 D-Index: 10 scientists 91 D-Index: 4 scientists 92 D-Index: 4 scientists 93+ D-Index: 100 scientists
30 D-Index 93+

This scientist: 43 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 93 D-Index or more.

Overview

Michael Kaliske is affiliated with TU Dresden in Germany and has a research focus within the field of Engineering, particularly in Mechanics of Materials, Civil and Structural Engineering, Mechanical Engineering, Biomedical Engineering, and Materials Chemistry. Their work covers a range of specific topics centered on numerical methods and material modeling.

The main topics of research include:

  • Numerical methods in engineering
  • Elasticity and Material Modeling
  • Composite Material Mechanics
  • Probabilistic and Robust Engineering Design
  • Fluid Dynamics Simulations and Interactions
  • Innovative concrete reinforcement materials
  • Fatigue and fracture mechanics

Recent papers authored or co-authored by Michael Kaliske include:

  • A ductile phase-field model based on degrading the fracture toughness: Theory and implementation at small strain (2020), Computer Methods in Applied Mechanics and Engineering
  • A review of carbon fiber surface modification methods for tailor-made bond behavior with cementitious matrices (2022), Progress in Materials Science
  • Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains (2023), International Journal for Numerical Methods in Engineering
  • DNN2: A hyper-parameter reinforcement learning game for self-design of neural network based elasto-plastic constitutive descriptions (2021), Computers & Structures
  • Crack phase-field model equipped with plastic driving force and degrading fracture toughness for ductile fracture simulation (2021), Computational Mechanics

Michael Kaliske has published extensively in a number of venues, with the most frequent publication outlets being:

  • PAMM
  • SSRN Electronic Journal
  • International Journal for Numerical Methods in Engineering
  • Tire Science and Technology
  • Computers & Structures

Collaborations have been formed with several frequent co-authors, including:

  • Johannes Storm
  • Bo Yin
  • Ines Wollny
  • Jakob Platen
  • Barış Cansız

The scientist has contributed to book publications as well, with a title published by Springer Nature:

  • Coupled System Pavement - Tire - Vehicle (2021)

Kaliske's publications span multiple subfields of study, reflecting a broad engagement across material and structural engineering disciplines. The integration of numerical methods and advanced modeling techniques signifies a focus on the computational aspects of engineering research.

Best Publications

  • Formulation and implementation of three-dimensional viscoelasticity at small and finite strains

    M. Kaliske;H. Rothert

  • An extended tube-model for rubber elasticity : Statistical-mechanical theory and finite element implementation

    M. Kaliske;G. Heinrich

  • A formulation of elasticity and viscoelasticity for fibre reinforced material at small and finite strains

    M. Kaliske

  • A phase-field crack model based on directional stress decomposition

    Christian Steinke;Michael Kaliske

  • Theoretical and numerical formulation of a molecular based constitutive tube-model of rubber elasticity

    G. Heinrich;M. Kaliske

  • Models for numerical failure analysis of wooden structures

    Jörg Schmidt;Michael Kaliske

  • A ductile phase-field model based on degrading the fracture toughness: Theory and implementation at small strain

    Bo Yin;Michael Kaliske

  • Bergström–Boyce model for nonlinear finite rubber viscoelasticity: theoretical aspects and algorithmic treatment for the FE method

    Hüsnü Dal;Michael Kaliske

  • A gradient enhanced plasticity---damage microplane model for concrete

    Imadeddin Zreid;Michael Kaliske

  • A review of carbon fiber surface modification methods for tailor-made bond behavior with cementitious matrices

    Unknown

  • On the finite element implementation of rubber‐like materials at finite strains

    M. Kaliske;H. Rothert

  • Numerical characterisation of uncured elastomers by a neural network based approach

    Unknown

  • Thermo-mechanically coupled investigation of steady state rolling tires by numerical simulation and experiment

    R. Behnke;M. Kaliske

  • Constitutive approach to rate-independent properties of filled elastomers

    M. Kaliske;H. Rothert

  • A micro-continuum-mechanical material model for failure of rubber-like materials: Application to ageing-induced fracturing

    Hüsnü Dal;Michael Kaliske

  • On damage modelling for elastic and viscoelastic materials at large strain

    Michael Kaliske;Lutz Nasdala;Heinrich Rothert

  • Fracture simulation of viscoelastic polymers by the phase-field method

    Bo Yin;Michael Kaliske

  • A fully implicit finite element method for bidomain models of cardiac electromechanics

    Hüsnü Dal;Serdar Göktepe;Michael Kaliske;Ellen Kuhl;Ellen Kuhl

  • An efficient viscoelastic formulation for steady-state rolling structures

    L. Nasdala;M. Kaliske;A. Becker;H. Rothert

  • Recurrent Neural Networks for Uncertain Time-Dependent Structural Behavior

    Wolfgang Graf;Steffen Freitag;Michael Kaliske;Jan-Uwe Sickert

  • Three-dimensional numerical analyses of load-bearing behavior and failure of multiple double-shear dowel-type connections in timber engineering

    Eckart Resch;Michael Kaliske

  • Recurrent neural networks for fuzzy data

    Steffen Freitag;Wolfgang Graf;Michael Kaliske

Frequent Co-Authors

Gert Heinrich
Gert Heinrich TU Dresden
Stefanie Reese
Stefanie Reese RWTH Aachen University
Markus Oeser
Markus Oeser RWTH Aachen University
Ellen Kuhl
Ellen Kuhl Stanford University
WaiChing Sun
WaiChing Sun Columbia University
Kenjiro Terada
Kenjiro Terada Tohoku University
Ralf Müller
Ralf Müller BI Norwegian Business School
Adib A. Becker
Adib A. Becker University of Nottingham

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