Stochastic Lifestyle

I often have people ask how I’m using Claude code so much, given that I have a bot account storming the SciML Open Source Software repositories with tens to hundreds of PRs a day, with many of them successful.

You may have seen the benchmark results and thought, “how the heck are the Julia ODE solvers on GPUs orders of magnitude faster than the GPU-accelerated Python libraries, that can’t be true?”

Claude Code sucks but is still useful: experiences maintaining Julia’s SciML scientific computing infrastructure So it’s pretty public that for about a month now I’ve had 32 processes setup on one of the 64 core 128gb RAM servers to just ssh in, tmux to a…

A very common adage in ODE solvers is that if you run into trouble with an explicit method, usually some explicit Runge-Kutta method like RK4, then you should try an implicit method.

I get it, you’re too good to vibe code.

We recently released a new manuscript Semi-Explicit Neural DAEs: Learning Long-Horizon Dynamical Systems with Algebraic Constraints where we showed a way to develop neural networks where any arbitrary constraint function can be directly imposed throughout…

Just how chaotic are chaotic systems?

Presented at JuliaEO25 This is a hands-on introduction to Scientific Machine Learning that does not assume a background in machine learning.

Component-based modeling systems such as Simulink and Dymola allow for building scientific models in a way that can be composed.

ISCL Seminar Series The Numerical Analysis of Differentiable Simulation: How Automatic Differentiation of Physics Can Give Incorrect Derivatives Scientific machine learning (SciML) relies heavily on automatic differentiation (AD), the process of construct…