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Bonsoir
Je me permets de vous contacter afin d’obtenir des informations et un encadrement concernant un projet sur lequel je travaille actuellement. Il s’agit d’une application visant à réguler un oscillateur quantique en potentiel harmonique à l’aide de l’intelligence artificielle.
Plus précisément, mon objectif est de :
Modéliser l’évolution de l’oscillateur quantique sous un potentiel harmonique.
Appliquer des techniques d’IA (réseaux de neurones, renforcement, etc.) pour optimiser la régulation de son état.
Analyser les performances des algorithmes dans le cadre de cette régulation.
Je souhaiterais savoir si vous pourriez m’apporter des conseils ou un encadrement dans la réalisation de ce projet, notamment sur les aspects mathématiques, physiques et computationnels impliqués. De plus, toute suggestion sur des références bibliographiques ou des outils adaptés (MATLAB, Python, TensorFlow, etc.) serait également très précieuse.
Dans l’attente de votre retour, Bien cordialement,
This topic is for discussing highlights to the current R2025a Pre-release.
📢 We want to hear from you! We're a team of graduate student researchers at the University of Michigan studying MATLAB Drive and other cloud-based systems for sharing coding files. Your feedback will help improve these tools. Take our quick survey here: https://forms.gle/DnHs4XNAwBZvmrAw6
[Last edited to fix a broken link]
I'm please to announce this new MATLAB Central feature for posting community highlights. This new channel will:
- Allow MathWorks and the community leaders to easily post newsworthy items to the community
- Allow community visitors to respond to these posts with Likes and Replies
- Allow anyone to follow/subscribe the channel so they can be notified of new posts
What do we mean by newsworthy? In short, this means anything we think some or all of the community might like to know. Here are some examples of what we’re thinking about posting to this new channel.
- New or upcoming community features or events
- User highlights (e.g. examples of good behavior, interesting posts)
- Interesting content (e.g. File Exchange pick of the week submissions)
- Release notes and new features
- Polls (future)
New highlights will appear on the community home page at the time they are posted and all past highlights can be found by going to the community home page and clicking the Highlights link in the right column.
As always, let us know what you think by liking this post or commenting below.
No
51%
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6%
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17%
Yes, I would like to know more
21%
Yes, I am cert. MATLAB Associate
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586 votes
Imagine you are developing a new toolbox for MATLAB. You have a folder full of a few .m files defining a bunch of functions and you are thinking 'This would be useful for others, I'm going to make it available to the world'
What process would you go through? What's the first thing you'd do?
I have my own opinions but don't want to pollute the start of the conversation :)
MATLAB FEX(MATLAB File Exchange) should support Markdown syntax for writing. In recent years, many open-source community documentation platforms, such as GitHub, have generally supported Markdown. MATLAB is also gradually improving its support for Markdown syntax. However, when directly uploading files to the MATLAB FEX community and preparing to write an overview, the outdated document format buttons are still present. Even when directly uploading a Markdown document, it cannot be rendered. We hope the community can support Markdown syntax!
BTW，I know that open-source Markdown writing on GitHub and linking to MATLAB FEX is feasible, but this is a workaround. It would be even better if direct native support were available.
Over the last 5 years or so, the highest-traffic post on my MATLAB Central image processing blog was not actually about image processing; it was about changing the default line thickness in plots.
Now I have written about some other MATLAB plotting behavior that I have recently changed to suit my own preferences. See this new blog post.
Here is a standard MATLAB plot:
x = 0:pi/100:2*pi;
y1 = sin(x);
y2 = cos(x);
plot(x,y1,x,y2)
I don't like some aspects of this plot, and so I have put the following code into my startup file.
set(groot,"DefaultLineLineWidth",2)
set(groot,"DefaultAxesXLimitMethod","padded")
set(groot,"DefaultAxesYLimitMethod","padded")
set(groot,"DefaultAxesZLimitMethod","padded")
set(groot,"DefaultAxesXGrid","on")
set(groot,"DefaultAxesYGrid","on")
set(groot,"DefaultAxesZGrid","on")
With those defaults changed, here is my preferred appearance:
plot(x,y1,x,y2)
I am glad to inform and share with you all my new text book titled "Inverters and AC Drives
Control, Modeling, and Simulation Using Simulink", Springer, 2024. This text book has nine chapters and three appendices. A separate "Instructor Manual" is rpovided with solutions to selected model projects. The salent features of this book are given below:
- Provides Simulink models for various PWM techniques used for inverters
- Presents vector and direct torque control of inverter-fed AC drives and fuzzy logic control of converter-fed AC drives
- Includes examples, case studies, source codes of models, and model projects from all the chapters
The Springer link for this text book is given below:
This book is also in the Mathworks book program:
Inverters and AC Drives: Control, Modeling, and Simulation Using Simulink
Successful development of power electronic converters and converter-fed electric drives involves system modeling, analyzing the output voltage, current, electromagnetic torque, and machine speed, and making necessary design changes before hardware implementation. Inverters and AC Drives: Control, Modeling, and Simulation Using Simulink offers readers Simulink models for single, multi-triangle carrier, selective harmonic elimination, and space vector PWM techniques for three-phase two-level, multi-level (including modular multi-level), Z-source, Quasi Z-source, switched inductor, switched capacitor and diode assisted extended boost inverters, six-step inverter-fed permanent magnet synchronous motor (PMSM), brushless DC motor (BLDCM) and induction motor (IM) drives, vector-controlled PMSM, IM drives, direct torque-controlled inverter-fed IM drives, and fuzzy logic controlled converter-fed AC drives with several examples and case studies.
To develop uifigure-based app, I wish MATLAB can provide something like uiquestdlg to replace questdlg without changing too much of the original code developed for figure-based app. Also, uiinputdlg <-> inputdlg and so on.
Are there parts of MATLAB that could disappear as far as you were concerned, things you don't need or which were "bad ideas" or which cause more trouble than they are worth in your experience?
One suggestion per answer please, so we can see how other people feel about the same matters.
It is time to support the cameraIntrinsics function to accept a 3-by-3 intrinsic matrix K as an input parameter for constructing the object. Currently, the built-in cameraIntrinsics function can only be constructed by explicitly specifying focalLength, principalPoint, and imageSize. This approach has drawbacks, as it is not very intuitive. In most application scenarios, using the intrinsic matrix
K=[fx,0,cx;
0,fy,cy;
0,0,1]
is much more straightforward and effective!
intrinsics = cameraIntrinsics(K)
In the past two years, large language models have brought us significant changes, leading to the emergence of programming tools such as GitHub Copilot, Tabnine, Kite, CodeGPT, Replit, Cursor, and many others. Most of these tools support code writing by providing auto-completion, prompts, and suggestions, and they can be easily integrated with various IDEs.
As far as I know, aside from the MATLAB-VSCode/MatGPT plugin, MATLAB lacks such AI assistant plugins for its native MATLAB-Desktop, although it can leverage other third-party plugins for intelligent programming assistance. There is hope for a native tool of this kind to be built-in.
On 27th February María Elena Gavilán Alfonso and I will be giving an online seminar that has been a while in the making. We'll be covering MATLAB with Jupyter, Visual Studio Code, Python, Git and GitHub, how to make your MATLAB projects available to the world (no installation required!) and much much more.
Sign up (it's free!) at MATLAB Without Borders: Connecting your Projects with Python and other Open-Source Tools - MATLAB & Simulink
Learn the basic of quantum computing, how to simulate quantum circuits on MATLAB and how to run them on real quantum computers using Amazon Braket. There will also be a demonstration of machine learning using quantum computers!
Details at MATLAB-AMAZON Braket Hands-on Quantum Machine Learning Workshop - MATLAB & Simulink. This will be led by MathWorker Hossein Jooya.
I kicked off my own exploration of Quantum Computing in MATLAB a year or so ago and wrote it up on The MATLAB Blog: Quantum computing in MATLAB R2023b: On the desktop and in the cloud » The MATLAB Blog - MATLAB & Simulink. This made use of the MATLAB Support Package for Quantum Computing - File Exchange - MATLAB Central
What is a rough number? What can they be used for? Today I'll take you down a journey into the land of prime numbers (in MATLAB). But remember that a journey is not always about your destination, but about what you learn along the way. And so, while this will be all about primes, and specifically large primes, before we get there we need some background. That will start with rough numbers.
Rough numbers are what I would describe as wannabe primes. Almost primes, and even sometimes prime, but often not prime. They could've been prime, but may not quite make it to the top. (If you are thinking of Marlon Brando here, telling us he "could've been a contender", you are on the right track.)
Mathematically, we could call a number k-rough if it is evenly divisible by no prime smaller than k. (Some authors will use the term k-rough to denote a number where the smallest prime factor is GREATER than k. The difference here is a minor one, and inconsequential for my purposes.) And there are also smooth numbers, numerical antagonists to the rough ones, those numbers with only small prime factors. They are not relevant to the topic today, even though smooth numbers are terribly valuable tools in mathematics. Please forward my apologies to the smooth numbers.
Have you seen rough numbers in use before? Probably so, at least if you ever learned about the sieve of Eratosthenes for prime numbers, though probably the concept of roughness was never explicitly discussed at the time. The sieve is simple. Suppose you wanted a list of all primes less than 100? (Without using the primes function itself.)
% simple sieve of Eratosthenes
Nmax = 100;
N = true(1,Nmax); % A boolean vector which when done, will indicate primes
N(1) = false; % 1 is not a prime by definition
nextP = find(N,1,'first'); % the first prime is 2
while nextP <= sqrt(Nmax)
% flag multiples of nextP as not prime
N(nextP*nextP:nextP:end) = false;
% find the first element after nextP that remains true
nextP = nextP + find(N(nextP+1:end),1,'first');
end
primeList = find(N)
Indeed, that is the set of all 25 primes not exceeding 100. If you think about how the sieve worked, it first found 2 is prime. Then it discarded all integer multiples of 2. The first element after 2 that remains as true is 3. 3 is of course the second prime. At each pass through the loop, the true elements that remain correspond to numbers which are becoming more and more rough. By the time we have eliminated all multiples of 2, 3, 5, and finally 7, everything else that remains below 100 must be prime! The next prime on the list we would find is 11, but we have already removed all multiples of 11 that do not exceed 100, since 11^2=121. For example, 77 is 11*7, but we already removed it, because 77 is a multiple of 7.
Such a simple sieve to find primes is great for small primes. However is not remotely useful in terms of finding primes with many thousands or even millions of decimal digits. And that is where I want to go, eventually. So how might we use roughness in a useful way? You can think of roughness as a way to increase the relative density of primes. That is, all primes are rough numbers. In fact, they are maximally rough. But not all rough numbers are primes. We might think of roughness as a necessary, but not sufficient condition to be prime.
How many primes lie in the interval [1e6,2e6]?
numel(primes(2e6)) - numel(primes(1e6))
There are 70435 primes greater than 1e6, but less than 2e6. Given there are 1 million natural numbers in that set, roughly 7% of those numbers were prime. Next, how many 100-rough numbers lie in that same interval?
N = (1e6:2e6)';
roughInd = all(mod(N,primes(100)) > 0,2);
sum(roughInd)
That is, there are 120571 100-rough numbers in that interval, but all those 70435 primes form a subset of the 100-rough numbers. What does this tell us? Of the 1 million numbers in that interval, approximately 12% of them were 100-rough, but 58% of the rough set were prime.
The point being, if we can efficiently identify a number as being rough, then we can substantially increase the chance it is also prime. Roughness in this sense is a prime densifier. (Is that even a word? It is now.) If we can reduce the number of times we need to perform an explicit isprime test, that will gain greatly because a direct test for primality is often quite costly in CPU time, at least on really large numbers.
In my next post, I'll show some ways we can employ rough numbers to look for some large primes.
Primes and rough numbers, used constructively to search for large p...
How can we use roughness in an effective context to identify large primes? I can quickly think of quite a few examples where we might do so. Again, remember I will be looking for primes with not ...
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Good day I am looking someone to help me on the matlab and simulink I am missing some explanations.For easy communication you can contact 0026876637042
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