This talk deserves good timestamps, but now we have only these. YT displays its length in weird way, more information here.
Contents
00:00 Welcome!
00:53 Obligatory huge disclaimer
01:32 First part of the talk: what does science need from code?
02:30 The one more important requirement: performance of "doing science"
02:48 Other requirements of scientists
03:25 What we all know and love
04:06 This talk is about "unspoken" powers of Julia
04:44 Syntax: clarity through the roof
05:15 Custom infix operators
05:52 Broadcasting (dot-fusion)
06:15 Design: unlimited productivity
07:22 Functions that mutate by convention end with "!"
07:41 Robust and reproducible science
08:48 Second part of the talk: JuliaDynamics
09:00 DynamicalBilliards.jl package
09:44 Unique features of DynamicalBilliards.jl
10:03 How to simulate a Billard?
10:48 Implementing function collisiontime in Julia results in clear and intuitive code
11:49 Performance? No problem
12:33 DynamicalSystems.jl, winner of SIAM DSWeb 2018 Software Contest
13:09 Crash-course: dynamical systems
13:49 Crash-course: Lyapunov exponent
15:51 Julia allow 1-to-1 code-algorithm correspondence
16:38 Why this code-algorithm correspondence in Julia is so great?
17:12 How fast is this code?
18:19 Manipulating functions in Julia is great
19:47 Summary
20:09 JuliaMusic is unrelated to dynamical systems, but it also great
20:19 Thank you!
21:05 Q&A: how performance of computing Lyapunov exponents compare to other packages?
22:10 Q&A: can you compute Feigenbaum constants?
22:43 Q&A: does your packages can analyze stability of fix points?
23:39 Q&A: do particles in DynamicalBilliards.jl interacts with each others?
24:18 Q&A: in the light of previous question, what "magnetic propagation" means?
24:53 Q&A: can you comment on how Julia help with extending such models?
Why Julia is the most suitable language for science, https://www.youtube.com/watch?v=7y-ahkUsIrY
This talk deserves good timestamps, but now we have only these. YT displays its length in weird way, more information here.
Contents 00:00 Welcome! 00:53 Obligatory huge disclaimer 01:32 First part of the talk: what does science need from code? 02:30 The one more important requirement: performance of "doing science" 02:48 Other requirements of scientists 03:25 What we all know and love 04:06 This talk is about "unspoken" powers of Julia 04:44 Syntax: clarity through the roof 05:15 Custom infix operators 05:52 Broadcasting (dot-fusion) 06:15 Design: unlimited productivity 07:22 Functions that mutate by convention end with "!" 07:41 Robust and reproducible science 08:48 Second part of the talk: JuliaDynamics 09:00 DynamicalBilliards.jl package 09:44 Unique features of DynamicalBilliards.jl 10:03 How to simulate a Billard? 10:48 Implementing function collisiontime in Julia results in clear and intuitive code 11:49 Performance? No problem 12:33 DynamicalSystems.jl, winner of SIAM DSWeb 2018 Software Contest 13:09 Crash-course: dynamical systems 13:49 Crash-course: Lyapunov exponent 15:51 Julia allow 1-to-1 code-algorithm correspondence 16:38 Why this code-algorithm correspondence in Julia is so great? 17:12 How fast is this code? 18:19 Manipulating functions in Julia is great 19:47 Summary 20:09 JuliaMusic is unrelated to dynamical systems, but it also great 20:19 Thank you! 21:05 Q&A: how performance of computing Lyapunov exponents compare to other packages? 22:10 Q&A: can you compute Feigenbaum constants? 22:43 Q&A: does your packages can analyze stability of fix points? 23:39 Q&A: do particles in DynamicalBilliards.jl interacts with each others? 24:18 Q&A: in the light of previous question, what "magnetic propagation" means? 24:53 Q&A: can you comment on how Julia help with extending such models?
Resources JuliaDynamics JuliaMusic DynamicalSystems.jl DynamicalBilliards.jl DSWeb 2018 Software Contest Feigenbaum constants