claims quite fast


bare metal julia arduino

println("hello world")
using Plots
using JuMP
using LinearAlgebra
x = [1,2,3,5]
using Metatheory
time echo "
println(\"hello world\")
using Plots
#using JuMP
#using LinearAlgebra
x = [1,2,3,5]
#using Metatheory
" | julia -i

Time to first plot is 2 seconds now. Not bad. It seems mad about something qt.qpa.plugin: Could not find the Qt platform plugin "wayland" in ""

hmm if I want julia to work good, I need to make a command that is valid julia but also valid bash

= =# multiline julia is not a bash comment

bash code julia won't run
# =# julia code bash won't run
# =# julia code bash won't run
"julia": "#=\njulia\n# =#\ninclude($dir * \"$fileName\")",

Yes, that’s right, Mr. Vice President. I am a genius.

packagecompiler make sys images so that packages load faster


If you want just linear problems, you can also try Tulip (this is me self-advertising; I wrote it). It’s pure Julia, and should give you decent performance. If you want nonlinear problems, among the open-source pure Julia solvers, you have: COSMO, which supports several cones & quadratic objective. It’s based on ADMM, same as SCS/OSQP Hypatia, which supports the largest variety of cones (especially the ones you’ve never heard of). It’s based on interior-point, same as ECOS/Mosek. For LP and convex QP, there is also



Evan’s new talk. Seems really cool. Categories for multiphysics?

Interesting project ideas:

  • PyRes translation
  • That prolog engine
  • SMT modulo Convex
  • Interactive Proofs
  • probabilistic games use homotopy continuation
  • Guarded rewrite rules
  • Constraint programming compilation
  • CHC from SSA
  • WP
  • anyon
  • linear relations / modules

Scientific Computing in Julia. Numerical Computing in Julia HPC in Julia Data Science in Julia Deep Learning in Julia Algorithm Design in Julia Physics for Programmers

Audience: Someone at my level or higher? People who do scientific computing? At labs? Engineers? Grad students? Hobbyists?

End Expectations: ? No one actually reads books

that optimization book in julia

Strang Book

Fluid Solver Wave Solver Fitting Data - An Inverse Problem Particle simulation Convnet ODE and PDE

Function Breaks, Type Stability Examining Code, llvm and native Fast Loops SIMD Parallelism GPU DSLs Partial Evaluation / Macros. generative functions Dispatch - Fast matrix overloading

Minimal: you can activate a environment.

From a julia repl, you can press ] to put it into Pkg mode

<code>pkg> activate .</code>

Revise.jl - You can use Revise.jl. If you’re editting a one off file, you can bring it into the repl with includet so that it automatically reloads anytime you change the file.

<code>julia> using Revise
julia> includet("myfile.jl")
julia> myfunction(7)

You should take a gander

This is how you get those slick little badges for documentation and

Unit testing. I don’t write tests for my code often enough, I know it’s a good thing to do. Here’s how you do it.

<code>using MyPkg
using Test

@testset "MyPkg.jl" begin
    # Write your tests here.
    @test true
    @test == 4

<code>pkg> test MyPkg</code>

JuliaCon 23


“global integrated assessment models” IAM simulating economics? earth4all World3 model introduced in the book Dynamics of Growth in a Finite World (1974). plotlyjs.jl



differentiable gpu ParallelStencil.jl, ImplicitGlobalGrid.jl and Enzyme.jl J Solving PDEs in parallel on GPUs with Julia - ETH course Enzyme Parallelstencil Optim CairoMakie

@parallel @views

using LinearAlgebra
[1 2; 3 4] # matrix
[1,2,3] # vector
Bidiagonal([2,2,2,2], [-1,-1,-1], :U)

Hmm. Differentiating through a forward problem.

Broadcasting with .

Image processing Images.jl

using Images

juliaup My how the times change juliaup add release juliaup default release juliaup update release

using Pkg


parquet for large data

using DataFrames
using Parquet2
using Random using Statistics
using StatsBase
@info "here we gooo"
using DataFrames

run(`echo foo`)

NeuralODEs functional model interface. What

  • FMI.jl: High level loading, manipulating, saving or building entire FMUs from scratch
  • FMIImport.jl: Importing FMUs into Julia
  • FMIExport.jl: Exporting stand-alone FMUs from Julia Code
  • FMICore.jl: C-code wrapper for the FMI-standard
  • FMIBuild.jl: Compiler/Compilation dependencies for FMIExport.jl
  • FMIFlux.jl: Machine Learning with FMUs (differentiation over FMUs)
  • FMIZoo.jl: A collection of testing and example FMUs

hypterparamter optimization DifferentiableEigen.jl


  • Chen T Q, Rubanova Y, Bettencourt J and Duvenaud D. 2018. Neural ordinary dierential equations. (Preprint 1806.07366) URL
  • Tobias Thummerer, Johannes Stoljar and Lars Mikelsons. 2022. NeuralFMU: presenting a workflow for integrating hybrid neuralODEs into real-world applications. Electronics 11, 19, 3202. DOI: 10.3390/electronics11193202
  • Tobias Thummerer, Lars Mikelsons and Josef Kircher. 2021. NeuralFMU: towards structural integration of FMUs into neural networks. In Martin Sjölund,
  • Lena Buffoni, Adrian Pop and Lennart Ochel (Ed.). Proceedings of 14th Modelica Conference 2021, Linköping, Sweden, September 20-24, 2021. Linköping University Electronic Press, Linköping (Linköping Electronic Conference Proceedings ; 181), 297-306. DOI: 10.3384/ecp21181297
  • Tobias Thummerer, Johannes Tintenherr and Lars Mikelsons. 2021. Hybrid modeling of the human cardiovascular system using NeuralFMUs. Journal of Physics: Conference Series 2090, 1, 012155. DOI: 10.1088/1742-6596/2090/1/012155
  • Bezanson J., Karpinsky S., Shah V. B. and Edelman A. 2012. Julia: A fast dynamic language for technical computing. (Preprint 1209.5145) URL
  • Danquah, B. Component Library for Full Vehicle Simulations repository on GitHub. Available online: (accessed on 4 October 2022).
  • Tobias Thummerer, Artem Kolesnikov, Julia Gundermann, Denis Ritz and Lars Mikelsons. 2023. Paving the way for Hybrid Twins using Neural Functional Mock-up Units. Proceedings of 15th Modelica Conference 2023, Aachen, Germany, October 9-11, 2023. (submitted Paper)
  • Tobias Thummerer, Lars Mikelsons. 2023. Eigen-informed NeuralODEs: Dealing with stability and convergence issues of NeuralODEs. arXiv.
  • Rackauckas, Christopher and Ma, Yingbo and Martensen, Julius and Warner, Collin and Zubov, Kirill and Supekar, Rohit and Skinner, Dominic and Ramadhan, Ali. 2020. Universal differential equations for scientific machine learning. arXiv preprint arXiv:2001.04385 Physics-enhanced Neural Ordinary Differential Equations (PeNODEs) Combine ODE models but allow fitting to data

Genie Stipple ORM Make dahsabords

sciml and ode

symbolicregression Miles cranmer

modelling toolkit pharmcometrics SIR model universal approximators using ODEs differentiate through ODEs memont closure approximations

compressed sensing mri


dyve - catlab to sciml bridge? isdef.jl SARprocessing.jl - synthetc aperture radar PmmAocsSimulator.jl. SatelliteToolbox.jl, ReferenceFrameRotations.jl, Snoopcompile how much time in llvm threadpinning quantom optics fluxml lux.jl some other neural thing scrna-seq data single cell autoencdoer?

3d u-net of liver-ct biomedical ct scan segmentation decision analysis vs stochastic programming “influence diagram” sorting algorithms


develop --local Bed puts local version Pkgtemplate.js

bar(x) = x + 4
foo(x) = bar(x) * 3

@code_llvm 1 + 2
@code_llvm foo(7)

seahorn llbmc crucible maybe surely trail of bits has something CBMC, SMACK, KLEE and SYMBIOTIC.

Genomics Bowtie is an ultrafast, memory-efficient short read aligner. Ben angmead

snakemake python make files? cf-core

@show ARGS

BED files genomicfeatures.kl BED, GFF3, bigWig and bigBed.

Tensoroperations.jl @tensor D[i,j,k] := A[i,l,k] * ... Tensornetwork.jl @tensoropt contraction order hmm, could triejoin be useful for tensorf contraction? low rank summary? summation?

TensrKit.jl waterlily.jl Prove-It EuclidZ3 Graph coloring. Approximate graph compression?

graph alignement. graphoptim.jl

geometric algebra

sound syntehsis

Julia on arduino pronto trajectory optimization

transport MParT.jl