Research topics

Our research focuses on the development of advanced computational methods and modeling approaches for nonlinear, multiscale, and multiphysics problems in mechanical engineering.

With these novel methodologies, we aim to (1) enable more accurate, efficient, flexible, and robust numerical modeling and simulation; (2) bridge the gap between computer-aided design, simulation, design optimization, and fabrication; and (3) combine classical, physics-based approaches with data-driven, physics-informed machine learning.

These developments are crucial for the engineering adoption of many advanced and emerging manufacturing and materials technologies, and to fully exploit their potentials. Furthermore, they are integral parts of the development of digital twins of cyber-physical systems that facilitate more efficient and sustainable product development and operation.

Topics

  1. Physics-based modeling and data-driven model development for engineering problems:
    • Nonlinear continuum mechanics (finite deformations, hyperelastic materials, viscoelasticity, plasticity, contact)
    • Structural mechanics, in particular 3D beam models
    • Multiscale modeling with classical and generalized continuum theories
    • Multiphysics models that couple mechanical with thermal, electro-magnetic, or chemical effects
    • Physics-guided machine learning for constitutive and surrogate modeling
  2. Development of advanced computational methods for engineering simulation and design optimization:
    • Isogeometric analysis, finite element and collocation methods
    • Topology, shape, design and sizing optimization
    • Homogenization methods for multiscale simulation
    • Surrogate modeling concepts for dynamical systems
  3. Advanced manufacturing applications and seamless integration of computational design, optimization and fabrication:
    • Multi-material 3D printing of functionally graded structures
    • 4D printing with soft active materials
    • Functional, 3D printed lattice structures and metamaterials
    • 3D knitting of functional textiles

Funded projects

Project title Partners Duration Sponsor
A thermodynamically consistent, inelastic constitutive modeling framework based on artificial neural networks - 2022-2025 DFG
Microlattice structures for lithium-ion battery electrodes: Chemo-mechanical beam modeling of diffusion-induced instabilities and optimal design Prof. Dr Bai-Xiang Xu (TU Darmstadt) 2021-2024 DFG
Data-driven methods for nonlinear multi-scale computational mechanics Prof. Dr. Kristian Kersting (TU Darmstadt) 2019-2021 TU Darmstadt

Research data and codes