Stories by ChrisRackauckas

Trade-Offs in Automatic Differentiation: TensorFlow, PyTorch, Jax, and Julia

Using machine learning to derive black hole motion from gravitational waves

Implications of Delayed Reopening in Controlling the Covid-19 Surge in USA

Composability in Julia: Implementing Deep Equilibrium Models via Neural ODEs

Abstractdifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia

Abstractdifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia

Julia for Biologists

Useful algorithms that are not optimized by Jax, PyTorch, or TensorFlow

Shadowing Methods for Forward and Adjoint Sensitivity Analysis of Chaotic System

ModelingToolkit, Modelica, and Modia: The Composable Modeling Future in Julia

Generalizing Automatic Differentiation: Uncertainty, Stability, and Parallelism

JuliaSymbolics Roadmap: A Modern Computer Algebra System for a Modern Language

JuliaCall Update: Automated Julia Installation for R Packages

Bayesian Neural Ordinary Differential Equations

Cuda.jl 2.0

MIT-led team to develop software to help forecast space storms

Analyzing sources of compiler latency in Julia: method invalidations

Physics-informed neural networks (PINNs) solver on Julia

GPU-Accelerated Ode Solving in R with Julia, the Language of Libraries

SciML: Neural PDEs, Lie Groups, and Stochastic Delay Differential Equations

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