Author Archives: Christopher Rackauckas

JuliaSim: Building a Product which improves Open Source Sustainability

By: Christopher Rackauckas

Re-posted from: https://www.stochasticlifestyle.com/juliasim-building-a-product-which-improves-open-source-sustainability/

How do you build products that support open source communities? In this non-technical talk with OpenTeams I discuss how the MIT Julia Lab, PumasAI, and JuliaHub have all been essential pillars of the julialang opensource community in its goal to achieve sustainable open science. If you’ve ever been curious about what the difference is between the Julia Lab and JuliaHub is, the evolution of these groups, and what kinds of different contributions they make to the open source community, in this talk I go through as many details as I could!

The post JuliaSim: Building a Product which improves Open Source Sustainability appeared first on Stochastic Lifestyle.

JuliaSim: Building a Product which improves Open Source Sustainability

By: Christopher Rackauckas

Re-posted from: https://www.stochasticlifestyle.com/juliasim-building-a-product-which-improves-open-source-sustainability/

How do you build products that support open source communities? In this non-technical talk with OpenTeams I discuss how the MIT Julia Lab, PumasAI, and JuliaHub have all been essential pillars of the julialang opensource community in its goal to achieve sustainable open science. If you’ve ever been curious about what the difference is between the Julia Lab and JuliaHub is, the evolution of these groups, and what kinds of different contributions they make to the open source community, in this talk I go through as many details as I could!

The post JuliaSim: Building a Product which improves Open Source Sustainability appeared first on Stochastic Lifestyle.

Symbolic-Numerics: how compiler smarts can help improve the performance of numerical methods (nonlinear solvers in Julia)

By: Christopher Rackauckas

Re-posted from: http://www.stochasticlifestyle.com/symbolic-numerics-how-compiler-smarts-can-help-improve-the-performance-of-numerical-methods-nonlinear-solvers-in-julia/

Many problems can be reduced down to solving f(x)=0, maybe even more than you think! Solving a stiff differential equation? Finding out where the ball hits the ground? Solving an inverse problem to find the parameters to fit a model? In this talk we’ll showcase how SciML’s NonlinearSolve.jl is a general system for solving nonlinear equations and demonstrate its ability to efficiently handle these kinds of problems with high stability and performance. We will focus on how compilers are being integrated into the numerical stack so that many of the things that were manual before, such as defining sparsity patterns, Jacobians, and adjoints, are all automated out-of-the-box making it greatly outperform purely numerical codes like SciPy or NLsolve.jl.

PyData Global 2023

The post Symbolic-Numerics: how compiler smarts can help improve the performance of numerical methods (nonlinear solvers in Julia) appeared first on Stochastic Lifestyle.