Professor
© TUD/NEFM
Professor for Computational and Experimental Solid Mechanics
NameProf. Dr.-Ing. habil. Markus Kästner
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Chair of Computational and Experimental Solid Mechanics
Visiting address:
Zeunerbau, Room 353 George-Bähr-Straße 3c
01069 Dresden
Research
Data-driven analysis of processes, materials and structures
- Microstructure characterisation and reconstruction
- Description of Process-Structure-Property (PSP) linkages
- Exploration of PSP linkages and optimization
- Virtual Sensing und clustering for load and stress analysis
- Digital twins for predictive maintenance
Development of data- and model-driven modeling techniques
- Physics-constrained neural networks for material modeling
- Data-driven multi-scale modeling with automated data augmentation
- Phase-field modeling of fracture and structural evolution processes
- Modeling of coupled boundary value problems
- Homogenization techniques for coupled problems
- Adaptive Isogeometric Analysis (IGA)
- Extended Finite Element Method (XFEM)
Experimental characterization and modeling of materials
- Damage and failure of additively manufactured materials
- Process-dependent fatigue behavior of materials
- Inelastic, rate-dependent material behavior of polymers
- Damage and failure of fibre reinforced composites
- Magnetosensitive elastomers and fluids
- Parameter identification and experimental validation of material models
Selected projects
- DFG-Priority Programme SPP 2489 DaMic
- DFG-Research Training Group GRK 2868 D³
- AMTwin - Data-driven analysis of processes, materials and structures for additive manufacturing
- ePredict - Predictive maintenance for electromobility
- LRVTwin - a digital twin for light rail vehicles
- Drucksache - multiscale characterization and modeling of additively manufactured lattice structures
Publications
2023
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DA-VEGAN: Differentiably Augmenting VAE-GAN for microstructure reconstruction from extremely small data sets, 20 Nov 2023, In: Computational materials science. 232 (2024), 14 p., 112661Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Fatigue crack growth in anisotropic aluminium sheets — phase-field modelling and experimental validation, Nov 2023, In: International Journal of Fatigue. 176, 107874Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Neural networks meet hyperelasticity: A guide to enforcing physics, Oct 2023, In: Journal of the Mechanics and Physics of Solids. 179, 105363Electronic (full-text) versionResearch output: Contribution to journal > Research article
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On the relevance of descriptor fidelity in microstructure reconstruction, 15 Sep 2023, In: PAMM. 23, 3, e202300116Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Scattering transform in microstructure reconstruction, 12 Sep 2023, In: PAMM. 23, 3, e202300169Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Experimental and numerical characterization of imperfect additively manufactured lattices based on triply periodic minimal surfaces, Sep 2023, In: Materials and Design. 233(2023), 18 p., 112197Electronic (full-text) versionResearch output: Contribution to journal > Research article
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A comparative study on different neural network architectures to model inelasticity, 18 Jul 2023, In: International Journal for Numerical Methods in Engineering. 124, 21, p. 4802-4840, 39 p.Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Shape optimization of additively manufactured lattices based on triply periodic minimal surfaces, 5 Jul 2023, In: Additive Manufacturing. 73(2023), 9 p., 103659Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Two-stage 2D-to-3D reconstruction of realistic microstructures: Implementation and numerical validation by effective properties, 1 Jul 2023, In: Computer methods in applied mechanics and engineering. 412, 116098Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Conditional diffusion-based microstructure reconstruction, Jun 2023, In: Materials today communications. 35, 105608Electronic (full-text) versionResearch output: Contribution to journal > Research article
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