Class
Digital methods not only allow to design differently they also offer great insight into the performance of buildings before being realised. To develop the expertise to design and evaluate architecture with the latest methods from data science, Artificial Intelligence (AI), we offer a combination of two joint seminars by the chair of Digital Methods in Architecture, dMA and the chair of Sustainable Building Systems. dMA will provide the computational design methods to generate parametric variations of a given design intent. Sustainable Building Systems will provide the energy simulation and AI expertise to evaluate the performance of a given design candidate.
Designing for Energy Efficiency Architecture - Computational Design by dMA covers the following topics.
- The generation of architectural form
- The discretisation of architectural form into building elements
- The parametrisation of architectural elements, adaptive components
- Developing parametric variations of a given design intent
- Defining and navigating the solution space of a given design intent
- Searching the design space for optimal solutions using Genetic Algorithm methods
A design for a greenhouse, urban farm, vertical garden will serve as design context.
Both courses will use Rhino Grasshopper and various plug-ins as the main platform. Knowledge of Rhino Grasshopper is helpful but not mandatory. All necessary techniques will be revisited at the beginning of semester.
It is mandatory to commit to both courses as they are interdependent.
Both courses will be thought in English.
Info
Modules:
- MSc. Architektur und Städtebau:
Seminar: Digitale Simulation und Visualisierung - BSc. Architektur:
Seminar: Datenräume
Lecturers:
- Prof. Mirco Becker
- Hendrik Wiese
Schedule:
- First Class: Friday 14.10.2022
12:00 – 14:00 Pool 3
- Colloquium: 09.12.2022 10:00 – 14:00 small Foyer
- Final presentation: 20.01.2023 10:00 – 14:00 small Foyer
Prerequisites:
- Enrollment in the class Designing for Energy Efficiency Architecture - Simulation and AI
Further info on stud.ip
