Sam Buercklin is a research software engineer at Metalenz.
At Metalenz, Sam develops computational E&M tools for optical metasurfaces. His work touches on high performance computing, numerical optimization, and user-facing tools to specify and solve complex technical problems.
Sam has previously worked in various computational domains, including quantum computing, optics, and neuroscience.
Tools for performing autodifferentiation (AD) and dimensional work in Julia are robust, but not always compatible. This talk explores how we can understand rule-based AD in Julia by showing how to make dimensional quantities from
Unitful.jl compose with
ChainRules.jl. Combining these two projects produces an intuitive look at the building blocks of AD in Julia using only rudimentary calculus and dimensional analysis.