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 inverse materials design
- 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
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
2025
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Characterization, modeling and validation of the crystallization kinetics of carbon fiber-reinforced polyamide-6 composites, 19 Jun 2025, In: Composites Part A: Applied Science and Manufacturing. 198, 20 p., 109077Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Accessing process-property linkages of polyamide 12 manufactured by the multi jet fusion printing process, 25 May 2025, In: Progress in Additive Manufacturing. 116882Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Neural networks meet phase-field: A hybrid fracture model, May 2025, In: Computer Methods in Applied Mechanics and Engineering. 440, 117937Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Schwingfestigkeit von Clinchverbindungen aus Kupfer für elektrotechnische Anwendungen, May 2025, Auslegung von Bauteilen aus Kupfer und Kupferlegierungen - Tagung 2025. Deutscher Verband für Materialforschung und -prüfung e.V., p. 93-102, 10 p.Electronic (full-text) versionResearch output: Contribution to book/conference proceedings/anthology/report > Conference contribution
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Experimental and numerical investigation of the evolution of residual stresses under cyclic mechanical loading, 17 Apr 2025, In: Archive of Applied Mechanics. 95, 5, 101Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Phase-field models for ductile fatigue fracture, Apr 2025, In: Theoretical and applied fracture mechanics. 136, 104842Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Inverse design of spinodoid structures using Bayesian optimization, 19 Feb 2025, In: Computational mechanics : solids, fluids, engineered materials, aging infrastructure, molecular dynamics, heat transfer, manufacturing processes, optimization, fracture & integrity. 104726Electronic (full-text) versionResearch output: Contribution to journal > Research article
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Fast descriptor-based 2D and 3D microstructure reconstruction using the Portilla-Simoncelli algorithm, Feb 2025, In: Engineering with computers. 41, 1, p. 589-607, 19 p., 113043Electronic (full-text) versionResearch output: Contribution to journal > Research article
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FFT‐Based Phase‐Field Fracture Modeling of Periodic Inhomogeneous Microstructures, 29 Jan 2025, In: Fatigue & Fracture of Engineering Materials & StructuresElectronic (full-text) versionResearch output: Contribution to journal > Research article
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Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functions, 21 Jan 2025, In: Computer Methods in Applied Mechanics and Engineering. 437, 117725Electronic (full-text) versionResearch output: Contribution to journal > Research article
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