2025-07-22 –, Main Room 2
DifferentialEquations.jl is the main package in Julia for solving differential equations. It has all sorts of things, from solving ordinary differential equations to stochastic differential equations, differential-algebraic equations, and more. You can switch to preconditioned GMRES linear solvers, exponential integrators, integrate with automatic differentiation, tweak the nonlinear solvers, and add customized stepping/logging routines. Most people only ever scratch the surface: let's dive in.
DifferentialEquations.jl is many things, and lots of people only use a small portion of it. The purpose of this workshop is to be accessible introduction to many aspects of the packages that the developers feel are underutilized and under-understood. We will start the workshop with a basic introduction to using the ODE solver so that everyone is comfortable, but then we will quickly start diving into detailed topics including:
- Scalability tips for ODEs: choosing the right solver, customizing linear solvers, integrating preconditioners, specializing the factorization routines, solving in mixed precision, and more.
- Understanding adaptive time stepping, controller choices, handling domain constraints, and details on advanced options.
- A deep dive into callbacks: performance of continuous callbacks vs vector continuous callbacks, early stopping of integration, the DiffEqCallbacks.jl premade library.
- Writing custom logging systems and taking direct control of the time stepping for embedded deployments.
- In-depth debugging of equations using the integrator interface, exposed solver error estimates, and integrations with Revise.
The workshop is made to go into details but should accessible to newcomers to the library who care about the details.
Dr. Chris Rackauckas is the VP of Modeling and Simulation at JuliaHub, the Director of Scientific Research at Pumas-AI, Co-PI of the Julia Lab at MIT, and the lead developer of the SciML Open Source Software Organization. For his work in mechanistic machine learning, his work is credited for the 15,000x acceleration of NASA Launch Services simulations and recently demonstrated a 60x-570x acceleration over Modelica tools in HVAC simulation, earning Chris the US Air Force Artificial Intelligence Accelerator Scientific Excellence Award. See more at https://chrisrackauckas.com/. He is the lead developer of the Pumas project and has received a top presentation award at every ACoP in the last 3 years for improving methods for uncertainty quantification, automated GPU acceleration of nonlinear mixed effects modeling (NLME), and machine learning assisted construction of NLME models with DeepNLME. For these achievements, Chris received the Emerging Scientist award from ISoP.
SciML and Base developer at JuliaHub