Am I right in understanding of Julia convention, that the birthday function should have actually been named birthday! because it acts to change its argument?
@gorsingКүн бұрын
Thanks
@cefeuchКүн бұрын
Thank you!!!
@rebauer2000Күн бұрын
Funny when you defined g as the plus function: g = + and then used g(3, 4), I stopped the video to try 3 g 4 - like 3 + 4. If I let played a few seconds longer, I would have seen, nope. I wasn't thinking like someone in audience.
@angelf.escalante78253 күн бұрын
Great tutorial! It would be great to know which minutes of the video correspond to which topic.
@Ulani153 күн бұрын
Hi can you show how you mesh your model in 3D ?
@hectormontenegro1646 күн бұрын
@sanbalestrini hi
@hugoferquiroz6 күн бұрын
Amazing! It's very simple to install!
@manchoiliew36948 күн бұрын
Great presentation! Thanks for your hard work, and I have read almost all of your papers on DEX routing and pathfinding.
@christbaumer8 күн бұрын
2:15 That's what she said.
@fugoogle_was_already_taken9 күн бұрын
07:00:33 horus
@christbaumer10 күн бұрын
Swag
@comatose378811 күн бұрын
This whole concept can clearly be shown within a few lines of super simple to understand code. Showing the reason this works so well. I know this is what you think you have here but, to a new coder this is far to complicated. When showing/explaining this style of programming, I also like to be going over top down programming. They fit together well.
@trejohnson767712 күн бұрын
not enough people using this.
@trejohnson767712 күн бұрын
shit is built in fortran because ur not beating the linalg libraries. that's it. the only reason. hundreds of thousands of man hours.
@IamZeus639113 күн бұрын
Hi, I'm new to julia and have to run a simulation that was written for julia 0.6. I was hoping that I can use juliaup to do that although as far as I understand 0.7 is the oldest available version. The problem I have right now are the old packages needed for the simulation like Dierckx and ProgressMeter. I just can't find any way to add them to the environment: (v0.7) pkg> add Dierckx ERROR: The following package names could not be resolved: * Dierckx (not found in project, manifest or registry) Please specify by known `name=uuid`. Also downloading old versions is not possible for a lot of packages because they are simply not available anymore. Is there an easy way I just don't know? Would be glad if someone knows an answer. best
@Downloader7717 күн бұрын
Excellent tutorial! Thanks
@aaronkaw485717 күн бұрын
Make a new programming language with multiple dispatch and borrow checking named Trust. Jokes aside, great presentation! I've downloaded Rust and will be giving it a shot.
@darkmatter958318 күн бұрын
where are the code from this channel posted? which github repo or which website?
@darkmatter958319 күн бұрын
where is the code?
@bandulamanchanayaka497120 күн бұрын
Thanks for your presentation, i am doing my final year project, "predictive modle for water quality using rheological properties of the water " I found RHEOS ,julia open source can apply for it.i don't have experience about modle creation, but I done through MATLB,can you please give me a guide,or relevant direction,
@ardra190521 күн бұрын
I am planning to use Javis now, but unfortunately the development has been idle for a couple of years on this project. This seems a lot more intuitive than Manim.
@mijstonen23 күн бұрын
Great talk. For sure Julia is at begin of life (also 5 years in the future, seen from the timeline of the video) and C++ is becoming more end of life (I say this - as a senior C++ specialist - because, whatever is added to C++, it was never designed for the current multi core architectures and HPC we have nowadays). But C++ is still the king here. You can archive the same thing with template specialization. With pro`s en contra`s. The pro is that you do not need dispatching. The abstract types are the templates whereas the instantiated types are the specialized argument types. From the C++ perspective this is a big plus, because there is no runtime overhead. No need to search through functions that match the pattern at runtime. But there are 2 disadvantages which fall in favor of Julia. The first is obvious, template specialization is hard to grasp and - with concepts on C++20 - it becomes even harder. The syntax is complex. Second, even if as good as, or arguable better then ...., it will become hard to convince teams to the common practice in the entire code base (what would be needed to come on the equal level as Julia). So in practice it will not be applied that way. But let finish in favor of C++. I have a choice what to do, static MD with template specialization or runtime polymorphism, and in fact many more options. Anyway, this sheds a new insight and a new light to system software architectures, maybe use of POD structures with template specialization becomes the future for maintainable C++ code bases ;-) . Not so much the end of C++ rather more that of initial Object Oriented intentions.
@fredpourlesintimes24 күн бұрын
Why are you frying? To mimic a man???
@kuijaye24 күн бұрын
Koobide? Nice choice
@B_knows_A_R_D-xh5lo25 күн бұрын
⭐️⭐️📚📚⭐️⭐️📚📚📚
@B_knows_A_R_D-xh5lo25 күн бұрын
📚📚📚📚📚⭐️⭐️📚📚📚📚📚
@absurde_yen25 күн бұрын
ColorQuantization
@tsukisan10125 күн бұрын
This seems very promising. Is it possible to use directly the code in an embedded device??? Or it must be transpile to some other language like C. Some experiences using Jetson or similar?? Anyway, the tool looks great.
@franckgaga22 күн бұрын
Thanks for the comments! Right now, running julia code on typical low power embedded device is not possible, but I belive that may be possible in a near future, knowing that some people is working on tools for that (e.g. StaticCompiler.jl and JuliaEmbedded org.). The alternative for now is using the embedded device only for the I/O manipulations and solving the MPC on a normal computer or a linux server for example. That's typically how (steady-state) real-time optimisation (RTO) is implemented in practice, by the way.
@fburton827 күн бұрын
Could someone please explain the picture reference in the title slide?
@leosmi127 күн бұрын
Nice
@oleksandrburylko982428 күн бұрын
Nice lecture. Please add saddle-node points to your pictures with red and blue arrows.
@herm244028 күн бұрын
7:14:40 EditBoundary.jl
@nataliaalamas28 күн бұрын
Very interesting! 👏🏼👏🏼👏🏼
@user-go5oe6td3kАй бұрын
I see a lot of these macros being thrown about. Is this language becoming like Java where every program is littered with countless "kinda common but not standard" libraries? Code reuse is obviously a useful thing, but having every program entangled with 100 different github projects is not good for maintainability, security or comprehension. Or, maybe I misunderstand?
@kamilziemian995Ай бұрын
I'm amazed by your work.
@cedrickhayat971Ай бұрын
Thanks this looks powerful ! Can trixi also adress simpler models such classic as linear or non linear static?
@rafaeljoaquimalves642Ай бұрын
5:32:33 Hydrological modelling
@rafaeljoaquimalves642Ай бұрын
6:36:45 PythonJL
@rafaeljoaquimalves642Ай бұрын
6:04:00 Julia Kitchen
@Hector-bj3lsАй бұрын
The github link is a 404. Looks like there are no public repositories on that account.
@B_knows_A_R_D-xh5loАй бұрын
awesome
@farooqpetersaidi7784Ай бұрын
Great Tutorial,Keep the fire burning 💕♥️
@aaronkaw4857Ай бұрын
As awesome as Frames' contribution to Julia is, you might want to change this video's title to match the actual speaker's name.
@benoizeАй бұрын
Man, I wish all tutorials were as clear and concise as this one. Great job! Excited to get to grips with Julia...
@AnikMukhopadhyayАй бұрын
In my experience of working with ITensor.jl , albeit it's elegance, it has quite a lot of disadvantages. The indices being separate entities rather than just dummy symbols is a double edged sword. It means you need to change your code significantly whenever index permutations are involved, and then the expressions start to significantly diverge from the easily readable einstein notation. I am also skeptical about it's speed, in my experience, it has always been outperformed by TensorOperations.jl or TensorKit.jl, especially considering the former also supports TBLIS.
@rauldurandАй бұрын
Is there a presentation about Julia's state and development?
@zdenekhurakАй бұрын
The very first talk on day 1, REPL stage: State of Julia kzfaq.info/get/bejne/hbeee9x5zNmUgaM.html
@SupGaillacАй бұрын
01:26:15 Accelerate Insights : Code→ Slides, Faster ! 01:36:00 Solving intergral equations with Inti.jl 02:09:20 Building Confidently in Julia with Interface Driven Design 02:38:00 AppBundler.jl - Bundle your Julia GUI Application 04:36:00 Unlocking lightning speed of Julia’s Dict with Robinhood Hashing 04:45:00 Simulation non-particle trajectoires using CMInject.jl 04:55:30 Natrual Science, Computer Modeling and Literate Programming 05:07:00 FUSE.jl : the power of Julia for the fusion industry 05:39:00 Geant4.jl - Particle transport in Julia 06:16:50 Lyotropic Liquid Crystals : Thermodynamics & Numerical methods 06:26:40 Efficient, composable solver for non-equilibrium flows 06:38:20 Jet Reconstruiction in Julia (not in this video) WGPU graphics and compute API in Julia 07:35:20 FastIce.jl : a massively parallel ice flow model running on GPUs