Like with past summers, I thought I'd share some of the things our summer researchers are investigating. In 2016, the team created computational prototypes focused on real-world workflows ranging from building analysis to geometric interoperability. In 2017, the focus was on data analysis and resulted in building data visualization workflows and the first version of … Continue reading Summer 2018 – Part 1
Last year, we introduced LunchBoxML - a machine learning (ML) plugin for Grasshopper and Dynamo that uses the Accord framework. LunchBoxML introduces several generalized supervised and unsupervised learning tools to visual programming including regression analysis, neural networks, and mixture models. We have published a few new examples to the Bitbucket repository to demonstrate the application … Continue reading New Machine Learning Examples with LunchBoxML
We have recently created a database framework called Minecart which harvests data from building information sources such as Revit and IFC formats. Our database is designed to represent building information as a scalable relational structure connecting data across multiple projects, documents, elements, and parameters.
This past summer, we released LunchBoxML for Grasshopper. Many have asked if we would publish a version for Dynamo. Ask no more! As of the last update, we have included the first version of LunchBoxML for Dynamo! By having this capability in Dynamo, users are able to start leveraging data from Revit to feed the … Continue reading LunchBoxML for Dynamo
