Chris Rackauckas

YouTube video by The Julia Programming Language SciML Developer Chat Episode 1: Trimming Support and Symbolics Precompilation

SciML Developer Chat Episode 1: Base Splits and Symbolics Precompilation

Welcome to the first episode of the SciML Dev Chat! We discuss the latest developments in the #SciML (Scientific Machine Learning) ecosystem for #julialang!

youtu.be/0yQ4aZ-ABhY

MIT Julia Lab: looking for postdoctoral researchers in #julialang open source software development, scientific machine learning (#SciML), and systems biological / pharmacological modeling (#QSP) for research in equation discovery for large stiff systems.

julia.mit.edu/projects/#po...

Claude Code in Scientific Computing: Experiences Maintaining Julia's SciML Infrastructure - Stochastic Lifestyle Claude Code in Scientific Computing: Experiences Maintaining Julia’s SciML 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 s...

Claude code sucks... but it's still very useful! Check out this blog post that details how Claude is deployed to solve problems in the #Julialang #SciML open source scientific computing infrastructure.

stochasticlifestyle.com/claude-code-...

#llm #vibecoding #physics #opensource #oss

YouTube video by JuliaHub Demonstration of AI-Assisted Modeling in Dyad with Cursor AI

AI-Assisted Modeling in Dyad with Cursor AI! We've just released a comprehensive demonstration showing how LLMs can dramatically accelerate dynamic system modeling workflows in Dyad.

youtu.be/awwJPWIlXXM?...

It's been a thrill to host Dr Chris Rackauckas for a 2-day visit to our node at QUT. Chris presented a seminar, ran a workshop on High-Performance Scientific Modelling with Julia & SciML, & met with our researchers. Chris is VP of Modelling and Simulation at JuliaHub. @chrisrackauckas.bsky.social

Scientific Modeling Cheatsheet: #MATLAB vs #Python vs #Julialang

Side-by-side comparison for:
• Differential equations
• Optimization
• Automatic differentiation
• Symbolic computing
• More!

Highlights ecosystem differences & best practices.

sciml.github.io/Scientific_M...

GitHub - SciML/Julia_Modeling_Workshop: High-Performance Scientific Modeling with Julia and SciML High-Performance Scientific Modeling with Julia and SciML - SciML/Julia_Modeling_Workshop

New workshop materials on High-Performance Scientific Modeling with Julia & SciML:

• #Julialang for scientific computing
• ODEs/PDEs & numerical methods
• Symbolic-numeric modeling
• Biological systems (Catalyst.jl)
• Parameter estimation
• #SciML & UDEs

github.com/SciML/Julia_...

YouTube video by The Julia Programming Language A Deep Dive Into DifferentialEquations.jl | JuliaCon Global 2025 | Rackauckas, Smith

DifferentialEquations.jl is many things, and lots of people only use a small portion of it. Check out the JuliaCon 2025 workshop: introduces many aspects of the packages that the developers feel are underutilized and under-understood!

#julialang #sciml

www.youtube.com/watch?v=lSGF...

Dr Chris Rackauckas will present a seminar at all three university stops: "Automating Construction of Biochemical and Physical Models with Scientific Machine Learning (SciML)"
📆Tues, 23 Sept: University of Melbourne
📆Thurs, 25 Sept: UNSW
📆Mon, 29 Sept: QUT
👉https://macsys.org/event/

MACSYS is excited to host Dr Chris Rackauckas for visits to three of our node universities. He is:
🔹VP of Modelling & Simulation - #JuliaHub
🔹Director of Scientific Research - #PumasAI
🔹Co-PI - Julia Lab at #MIT
🔹Lead developer - #SciML Open Source Software Org
@chrisrackauckas.bsky.social ( 🧵1/2)

YouTube video by The Julia Programming Language SciML in Fluid Dynamics (CFD): Surrogates of Weather Models | JuliaCon 2025 | Rackauckas, Abdelrehim

Check out the JuliaCon workshop SciML in Fluid Dynamics (CFD): Surrogates of Weather Models.
Go into depth on all of the major surrogate architectures and give them a try on the weather model challenge problem!

www.youtube.com/watch?v=PfRx...

#julialang #juliacon #sciml

SciML: Open Source Software for Scientific Machine Learning Open Source Software for Scientific Machine Learning

Sundials.jl v5.0: Update to SUNDIALS v7 and Improved DAE Initialization

A major update that brings significant improvements to differential-algebraic equation (DAE) solving and upgrades to the latest Sundials C library

sciml.ai/news/2025/09...

#julialang #sciml #sundials #dae

SciML: Open Source Software for Scientific Machine Learning Open Source Software for Scientific Machine Learning

SciML Fellowship development 2025 - JumpProcesses.jl: Introducing vr_aggregator for VariableRateJumps, GPU-enhanced SimpleTauLeaping and Extending with τ-Leap Algorithms for Jump Process Simulation

#julialang #ssa #gillespie #sciml #tauleap

sciml.ai/news/2025/09...

SciML: Open Source Software for Scientific Machine Learning Open Source Software for Scientific Machine Learning

Introducing SymbolicSMT.jl for symbolic constraint solving and theorem proving! Built on Z3, test the feasibility of symbolic expressions built using Symbolics.jl. Given Constraints([x > 0, y > 0, x^2 + y^2 <= 1]), ask issatisfiable? isprovable?

#julialang #symbolics #smt

sciml.ai/news/2025/09...

YouTube video by Julia Dispatch Juliet & Romeo with Jesper Öqvist and Philip Olhager

New episode of the #julialang Dispatch podcast, featuring Jesper and Philip from Cognibotics, a Swedish robotics company that developed a statically-typed dialect of Julia designed specifically for real-time robot programming!

www.youtube.com/watch?v=8wja...

Yes I'll be going all over Australia. Happy to connect with anyone while there.

MACSYS will host Dr Chris Rackauckas for visits to three of our node universities this month. He is:
🔹VP of Modelling & Simulation - #JuliaHub
🔹Director of Scientific Research - #PumasAI
🔹Co-PI - Julia Lab at #MIT
🔹Lead developer - #SciML Open Source Software Org
@chrisrackauckas.bsky.social

Newton's Method in Mixed Precision | SIAM Review We investigate the use of reduced precision arithmetic to solve the linear equation for the Newton step. If one neglects the backward error in the linear solve, then well-known convergence theory impl...

Yes, C Kelly has a whole analysis epubs.siam.org/doi/10.1137/.... Same guy as on the discourse btw

SciML: Open Source Software for Scientific Machine Learning Open Source Software for Scientific Machine Learning

Mixed precision and automatic GPU offloading for Newton solves and stiff ODE solvers has now landed thanks to improvements in LinearSolve.jl. Run LinearSolveAutotune.jl so that things like usage of GPUs in #julialang #sciml becomes automatic!

sciml.ai/news/2025/09...

Robust parameter estimation for rational ordinary differential equations We present a new approach for estimating parameters in rational ODE models from given (measured) time series data. In typical existing approaches, an …

New Symbolic-Numeric algorithm which uses rational polynomial interpolation mixed with differential algebra in order to give a highly robust method for parameter estimation and solving inverse problems on ODEs.

#julialang #sciml #symbolicnumeric #ode

www.sciencedirect.com/science/arti...

Implicit ODE Solvers Are Not Universally More Robust than Explicit ODE Solvers, Or Why No ODE Solver is Best - Stochastic Lifestyle 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. Implicit methods,...

New blog post: Implicit ODE Solvers Are Not Universally More Robust than Explicit ODE Solvers, Or Why No ODE Solver is Best

Talks about how "robust" methods can be less robust in practice. Justifies hundreds of methods in #julialang #sciml

www.stochasticlifestyle.com/implicit-ode...

There seems to be infinitely many things to do.

A Guide to Gen AI / LLM Vibecoding for Expert Programmers - Stochastic Lifestyle I get it, you’re too good to vibe code. You’re a senior developer who has been doing this for 20 years and knows the system like the back of your hand. Or maybe you’re the star individual contributor ...

"A Guide to Gen AI / LLM Vibecoding for Expert Programmers"

A perspective on the true role of Generative AI and LLM-Based Vibe Coding for the future of development.

www.stochasticlifestyle.com/a-guide-to-g...

#GenerativeAI #llm #claude #chatgpt #vibecoding

Yes, an also the aspect of using the simulation process or parts of its equations as part of the machine learning so that certain properties are enforced, not learned.

SciML: Open Source Software for Scientific Machine Learning Open Source Software for Scientific Machine Learning

LinearSolve.jl just got autotuning 🔥 The problem: picking linear solvers is hardware-dependent voodoo. Solution: automatic benchmarking that tells you EXACTLY what to use. Share the results to improve the system!

Details: sciml.ai/news/2025/08/16/linearsolve_autotuning/

#julialang #sciml

YouTube video by JuliaHub What is the Julia Programming Language?

What is the #julialang programming language? This is stitched from the interviews of a lot of developers telling their stories as to why they are committed to the mission of #Julia. Performance, scaling, easy, fun?

www.youtube.com/watch?v=jPDC...

#sciml #scientificcomputing #hpc #scaling

YouTube video by Julia Dispatch Julia Health with Jacob Zelko

#Julialang Dispatch: we welcome Jacob Zelko, who takes us on a fascinating journey from his early days as a #biomedical engineering student through his work at the CDC during the COVID-19 pandemic, and into his current exploration of applied #categorytheory.

www.youtube.com/watch?v=HyMp...

Well no one responded to libflame issues for 4 years and haven't tagged in 6. So at what point do you stop waiting and just say welp, here are the results. I thought 4 years was enough. It's on EPYC which isn't in their set I think? At least on that CPU it's not good.

BLIS is a really slow BLAS lol

Increasing spectral DCM flexibility and speed by leveraging Julia’s ModelingToolkit and automated differentiation Abstract. Using neuroimaging and electrophysiological data to infer neural parameter estimations from theoretical circuits requires solving the inverse problem. Here, we provide a new Julia language p...

This demonstrates how the optimization and Bayesian estimation of the Julia SciML tooling could be the tool analyzing your fMRIs in the near future!

See the link for the full paper:
direct.mit.edu/imag/article...