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The Special Math of Translating Theory to Software In Differential Eqs | Chris Rackauckas | ASE60 

The Julia Programming Language
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21 авг 2024

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Комментарии : 8   
@jonathandavis697
@jonathandavis697 11 месяцев назад
Awesome Chris! I'm hoping to get more of my coworkers on the Julia train but one of the first questions I always get from the grey-beards is "what are these solvers? how can I trust them and where is my ODE45." This talk is exactly what was needed!
@AnthonyDavid59
@AnthonyDavid59 3 месяца назад
When I started looking at Julia, one of the first things that impressed me was the richness and care put into its ODE solvers. While I have yet to use them in anger, this talk gives some context to my first impressions. Thank you.
@vasilin97
@vasilin97 11 месяцев назад
Great talk, thank you, Chris!
@roboticus3647
@roboticus3647 11 месяцев назад
Good to watch, but do yourself a favor and lower the playback speed to 75% because he *talks so, so fast*.
@sucim
@sucim 4 месяца назад
So good! Would be nice if you would spend like 30s to explain what e.g. stiff means (for noobs like me) but otherwise it was a really nice presentation. I was in the rk4 and chill camp but now I‘m higher order curious
@aaronkaw4857
@aaronkaw4857 4 месяца назад
Stiffness is when your ode solver needs a really small stepsize, otherwise it's unstable.
@sucim
@sucim 4 месяца назад
@@aaronkaw4857 Interesting! That makes it sound like a quantitative property (blurred lines on what constitutes a "small stepsize"). From the name it always appeared to be a qualitative differrence between systems 🤔
@aaronkaw4857
@aaronkaw4857 4 месяца назад
I hope Tsitouris sees this presentation, or at least knows his paper is being applied.
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