Workshops on computational design and generative art tend to start with a sense of excitement. The participants find themselves exhilarated as they discover that forms can be made to move and interact with just a few lines of code. But then a certain point is reached, where the words “trigonometry” and “vector” are mentioned. And often exhilaration turns to despair.
Regardless of whether you believe the old “right brain / left brain” clichee that creative people are bad at math and vice versa, there is a wall of knowledge that divides the scientist from the creatives. The old mistake is to think that the scientists have all the knowledge on their side, since they can to refer to physical laws and all kinds of theorems. The artists and designers are left with “soft” theories of communication and art history, much maligned by the rational scientific community. But put a physicist in charge of an advertising campaign, and you will most likely get a spectacular failure. In fact, it will be much like a nuclear reactor built by cubist painters.
Yet aesthetics is a field of knowledge, with massive amounts of empirical data to back it up. Advertising execs and industrial designers can refer to demographic studies, ergonomic principles and historical and cultural biases as to which color best expresses joy. But the artist is sometimes left with no option but to say “it is so”, without the faintest data to back her up. Still, no creative would doubt that any artist’s method is based on a mass of internalized knowledge. It’s just a shame it’s so hard to communicate.
A simple “you know stuff, too” pep-talk will never get creatives over the mathematics threshold. Some will give up, some will find unexpected resources within themselves and yet others will learn to build on work done by others. That’s where people like Paul Falstad come in handy.
Falstad has published a rich resource of Java applets demonstrating physical and mathematical principles, many of them with source code included. One can find wave simulations, vector fields, digital signal filters, magnetostatic fields and even quantum theory. And while this is still heady stuff, at least it’s in a visual form.
Want to model organic or mechanical motion? Go pay Craig Reynolds a visit, he created the classic Boids algorithm and has plenty of data and code online. This is essential reading for learning how to describe movement in terms of intention and action, rather than just as a set of changing X,Y coordinates.
The moral? There is hope. Any student who learns to google creatively will find help for even the most obscure problem.
(Via Andreas Nordenstam on BEK’s BB list.)