Parametric Hermeneutics
- Add renders from Matija
- Compile into animated GIFs or HTML sliders
- Write commentary and discussion based on renders and lessons from the other experiments
From Experiment
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Significance
Gap
Opportunity
Questions
Methods
Discussion
Potential
Limitations
Contribution
Future
Related projects
Project Description
Context
Objectives
Agents
Methods
Results
Discussion
Notes
Smart Spaces
- Mediated Atmospheres
- Extending research into use of atmosphere (lighting, air conditioning, other environmental parameters) beyond concerns about productivity at the office, e.g. health, mood, satisfaction, nonhuman issues, etc.
- Herztian Space
- Taylor, Mark. “Hertzian Space: Material Response to Spatial Presence.” Architectural Design 77, no. 5 (2007): 149–51. https://doi.org/10/dpjskt.
Machine Learning
- Atmosphere, mood and emotion
- Architecture
- Audio
- Cunningham, Stuart, Harrison Ridley, Jonathan Weinel, and Richard Picking. “Supervised Machine Learning for Audio Emotion Recognition: Enhancing Film Sound Design Using Audio Features, Regression Models and Artificial Neural Networks.” Personal and Ubiquitous Computing, April 22, 2020. https://doi.org/10/gkzjd4.
- Physical space
- Generating three-dimensional objects and spaces using machine learning techniques
- Wu, Jiajun, Chengkai Zhang, Tianfan Xue, William T. Freeman, and Joshua B. Tenenbaum. “Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling.” arXiv:1610.07584 [Cs], January 4, 2017.
- Tatarchenko, Maxim, Alexey Dosovitskiy, and Thomas Brox. “Octree Generating Networks: Efficient Convolutional Architectures for High-Resolution 3D Outputs.” arXiv:1703.09438 [Cs], 2017.
- Generating three-dimensional objects and spaces using machine learning techniques
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