In this webinar, we will explore the integration of Machine Learning and Parametric Models for the purpose of early design space exploration and optimization. Several case studies will be used to describe the different considerations and best-practices regarding setting up parametric models, generating and extracting data, applying learning tasks (classification vs regression), and optimizing, selecting & deploying models into production.
Wednesday, May 16, 2018, 10:00 pm
- 12:00 am
Duration: 2 hours
Who can attend
Dial-in available? (listen only)
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A quick set up of Anaconda and libraries
Case study 1: Parametric Energy Simulation (Machine Learning)
Case study 2: Daylight analysis (Machine Learning)
Case study 3: Urban solar radiation (Deep Learning)
Thoughts and future steps
Education & learning
Science & tech
Hosted By Thank God It's Computational
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Theodore Galanos is a Computational Environmental Designer focused on developing tools and processes that integrate important theoretical and technological developments from different fields into the AEC.