Markus Kästner

Engineering · Physical Sciences

36h-index3.5kcitations230works5.72yr avg
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Research Topics

Numerical methods in engineering(57), Composite Material Mechanics(54), Mechanical Behavior of Composites(33), Elasticity and Material Modeling(29), Vibration Control and Rheological Fluids(21)

Publications230 total

An Efficient Bayesian Framework for Inverse Problems via Optimization and Inversion: Surrogate Modeling, Parameter Inference, and Uncertainty Quantification
ArXiv.org·2026Open Access
An Efficient Bayesian Framework for Inverse Problems via Optimization and Inversion: Surrogate Modeling, Parameter Inference, and Uncertainty Quantification
arXiv (Cornell University)·2026Open Access
Structure–Property Relationships of Solid Oxide Fuel Cell Electrodes Using Real and Reconstructed Microstructures
International Journal for Numerical Methods in Engineering·2025Open Access
Conceptual design for a referenced large-scale drone-based optical measurement system
2025
Construction of irreducible integrity basis for anisotropic hyperelasticity via structural tensors
arXiv (Cornell University)·2025Open Access
A physics-augmented neural network framework for finite strain incompressible viscoelasticity
arXiv (Cornell University)·2025Open Access
Data-Efficient Inverse Design of Spinodoid Metamaterials
Integrating materials and manufacturing innovation·2025· 2 citedOpen Access
A dual-stage constitutive modeling framework based on finite strain data-driven identification and physics-augmented neural networks
Computer Methods in Applied Mechanics and Engineering·2025· 1 citedOpen Access
When invariants matter: The role of I1 and I2 in neural network models of incompressible hyperelasticity
Mechanics of Materials·2025· 6 citedOpen Access
Characterization, modeling and validation of the crystallization kinetics of carbon fiber-reinforced polyamide-6 composites
Composites Part A Applied Science and Manufacturing·2025· 2 citedOpen Access
Accessing process-property linkages of polyamide 12 manufactured by the multi jet fusion printing process
Progress in Additive Manufacturing·2025Open Access
Code and Data for: Data-efficient inverse design of spinodoid metamaterials
arXiv (Cornell University)·2025Open Access
A dual-stage constitutive modeling framework based on finite strain data-driven identification and physics-augmented neural networks
arXiv (Cornell University)·2025Open Access
Experimental and numerical investigation of the evolution of residual stresses under cyclic mechanical loading
Archive of Applied Mechanics·2025· 3 citedOpen Access
Neural networks meet phase-field: A hybrid fracture model
Computer Methods in Applied Mechanics and Engineering·2025· 11 citedOpen Access
When invariants matter: The role of I1 and I2 in neural network models of incompressible hyperelasticity
arXiv (Cornell University)·2025Open Access
Inverse design of spinodoid structures using Bayesian optimization
Computational Mechanics·2025· 9 citedOpen Access
FFT‐Based Phase‐Field Fracture Modeling of Periodic Inhomogeneous Microstructures
Fatigue & Fracture of Engineering Materials & Structures·2025· 1 citedOpen Access
Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functions
Computer Methods in Applied Mechanics and Engineering·2025· 19 citedOpen Access
Phase-field models for ductile fatigue fracture
Theoretical and Applied Fracture Mechanics·2025· 16 citedOpen Access
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Frequent Co-authors

Karl A. Kalina(15), Jörg Brummund(15), Philipp Metsch(9), Volker Ulbricht(7), W. Hirsch(7), Paul Hennig(6), Paul Seibert(6), Sebastian Müller(5), Marreddy Ambati(5), René de Borst(4), Lennart Linden(4), Alexander Raßloff(4), C. Spieler(3), Franz Dammaß(3), Arne Claus Hansen-Dörr(2), Max Rosenkranz(2), Tom Schneider(2), Joseph Goldmann(2), Dirk Romeis(2), Marina Saphiannikova(2)