Research topics

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.

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

https://github.com/CPShub