When I started MESH in 2012, I wanted to use my skills as a mathematician and developer to help solve geometrically challenging projects in industry settings. As my team of consultants grew, I welcomed the expertise of new colleagues in the areas of architecture, engineering, construction, and design. This newest adventure, Metafold, has emerged from the expertise of the MESH team, and in particular our specialty service of the design and 3D printing of Microstructures.
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The study of metamaterials aims to extract extraordinary behaviours from ordinary materials through careful design on the scale of microstructure. At MESH we are particularly interested in the role of geometry in determining the macrostructural behaviour of such an “architected material”.
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I developed a lot of respect for the work of data scientists last week, when I did a little project involving a data set of building permits for the city of Toronto. Like any good research project, it raised more questions than it answered, but I will record some of what I did learn here.
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Like everyone everywhere these days, architects want to use machine learning. They want machine learning to design their buildings, to streamline their documentation burden, to provide realtime simulation results, and to close the feedback loop between design and engineering. Unfortunately, architects have a data problem.
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Have you ever noticed that most illustrations in math articles look the same? The same basic style, the same LaTeX’ed labels, similar lineweights and the same arrowheads? Why is this? In this post I will discuss a few different methods to make (and not to make) mathematical pictures.
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