Patrick Jaap
I'm a postdoc researcher at the Weierstrass Institute in Berlin in the scientific computing group.
My day-to-day work is all about running finite element simulations for solid mechanics and quantum devices.
I came into touch with Julia two years ago and I love it since.
At WIAS, we have our own Julia PDE solver ecosystem WIAS-PDELib, where I am a core maintainer.
I'm interested in low-level solver routines, software architecture, coding quality, and experiments with new data types and programming features.
Session
In Float64, 0.1 + 0.2 – 0.3 is not zero due to round-off errors. Try it. Stochastic rounding provides a trick: floating point rounding is done probabilistically: sometimes up, sometimes down. This allows for numerical simulations on hardware made for machine learning, where Float16 precision is common. We can perform meaningful simulations even with limited bits.
And yes, all of this can be done in Julia.