Faster fluid modeling transforms civil engineering

A new method that uses neural networks to approximate partial differential equations exploits Fourier analysis, yielding a faster and much more general approach to modeling fluids with AI. This has huge implications for climate modeling—and cloud drive rapid and step-changing advances in how we model complex urban food, waste, water, transport and energy systems.