Using AI for Reduced-Order Modeling
mathinking
by Lucas García
5M ago
The following post was published on MathWorks’ AI blog. This blog discusses the MathWorks’ presence at NeurIPS 2022 and my talk on ‘Using AI for Reduced-Order Modeling’ at the conference. Figure: The MathWorks team at our booth at NeurIPS 2022 Founded in 1987, the Conference on Neural Information Processing Systems (abbreviated as NeurIPS) is one of the most prestigious and competitive international conferences in machine learning. Last week, the MathWorks team was at NeurIPS 2022 in New Orleans for the in-person portion of the conference. During the Expo Day at NeurIPS, I presented a talk ab ..read more
Visit website
My favorite features in MATLAB R2022a
mathinking
by Lucas García
2y ago
MATLAB R2022a was released this week and, as I have done for the previous two releases (R2021a, R2021b), I would like to highlight the features that I am most excited about. As usual, it has been hard to come up with this list. I have had to leave out many new features that I am truly eager about… Do not forget to check the release highlights with 5 new products and release notes to learn about updates to your favorite products. 10. pcode For all my years at MathWorks, every once in a while, I have been approached by users regarding pcode obfuscation. Protecting IP has always been a topic of i ..read more
Visit website
New adventures
mathinking
by Lucas García
2y ago
In the time before the iPhone and Instagram, before Uber and Netflix, I was a C++ developer working in the Finance industry (this was in 2007-2008, I am not that old… Oh, and I promise I didn’t have anything to do with the financial crisis). Although I was quite happy at my employer and really enjoyed what I was doing, an amazing opportunity came up for me. I had an interview with MathWorks, the creators of MATLAB! After a thorough interview process, I joined MathWorks as a Training Engineer. And for three and a half years, I delivered training courses on MATLAB, Simulink, and Toolboxes to eng ..read more
Visit website
How I met your MATLAB
mathinking
by Lucas García
2y ago
I was 16 when I first met your MATLAB. I remember having some math homework to do: Obtain the integral of function I do remember writing in my notebook: And that was as far as I got (on paper). After investing a considerable amount of time, I concluded that I could not solve this integral with the techniques that I had available. Could this be a non-integrable function? Why would our teacher give us non-solvable homework? Had I made a mistake while writing down the assignment from the blackboard? So, after being frustrated for a while, I thought I could maybe solve it numerically. Years ..read more
Visit website
My favorite features in MATLAB R2021b
mathinking
by Lucas García
2y ago
It’s yet again that time of the year. A new MATLAB release just came out. I know that there are plenty of great features that could have made it to the list, which doesn’t mean I don’t find them relevant or useful. These are the ones that I believe that will be more relevant to me in the coming months. So let’s cut to the chase and visit my favorite new features in MATLAB R2021b: 10. Exporting Animations in the Live Editor Exporting animations got easier with the Live Editor. You can export animations to movies or GIFs simply by using the new Export Animation button. Click the image to play t ..read more
Visit website
Part 5. Leverage the work from the MATLAB community
mathinking
by Lucas García
2y ago
This post is the fifth and final post on the series A trick you don’t know about Python: MATLAB. In the world of Data Science and Software Development, it’s fair to say that teams are constantly getting inspiration from each other, in the form of StackOverflow responses, Reddit threads, open-source code in GitHub, research articles published on arXiv, etc. When such work reaches a certain magnitude, it is typically shared as a package, library, or toolbox (for Python, R, or MATLAB) to be used by a broad community of data scientists, developers, and engineers. However, it might be challenging t ..read more
Visit website
Part 4. Want to validate conclusions in Python by running equivalent MATLAB code
mathinking
by Lucas García
2y ago
This post is the fourth post on the series A trick you don’t know about Python: MATLAB. For this example, I will be presenting an exciting benchmark initiated by my colleague Mike Croucher, a member of MathWorks’ Customer Success Engineering team in the UK. He works with academics around the country on different aspects of their teaching and computational research. If you haven’t already, you must visit his blog - as soon as you finish reading this entry. ;) When discussing with Mike why a Python user would use MATLAB, one frequent reason is validating your conclusions and results. Mike pointe ..read more
Visit website
When MATLAB Production Server met Thunder Client for VS Code
mathinking
by Lucas García
2y ago
Postman has been my preferred choice for quite some time when testing any MATLAB code that I am bringing to production or showcasing others how to leverage the RESTful API for MATLAB Production Server. However, I must say that I am a huge cURL fan. It is almost always available on test machines and is easy to use in scripts. Besides, I have great esteem for Daniel Stenberg, the man behind cURL. Recently, I started to play with Thunder Client, a lightweight Rest API Client Extension for Visual Studio Code. This Extension facilitates the creation of REST API calls while staying at VS Code. Here ..read more
Visit website
Part 3. Need functionality in Python only available in MATLAB (e.g. Simulink)
mathinking
by Lucas García
2y ago
This post is the third post on the series A trick you don’t know about Python: MATLAB. One apparent and yet frequent reason for calling MATLAB from Python is when you need to use a specific functionality not available in Python. Such a scenario might involve using Simulink, code generation functionality, or the use of particular engineering libraries (i.e., wireless communications, controls, aerospace, biology, etc.). For this post, I’d like to showcase the workflow of using Python and Simulink together. As I already showed with part 1 and part 2, it all begins with starting the MATLAB Engine ..read more
Visit website
Part 2. Facilitate Python development by using a simplified MATLAB workflow
mathinking
by Lucas García
2y ago
This post is the second post on the series A trick you don’t know about Python: MATLAB. Data labeling, selecting suitable features for your problem, choosing the appropriate model from dozens of options, running multiple deep learning experiments, etc. You might find occasions where the development of such a task in Python can be tedious and time-consuming. To this end, the following examples showcase how you can facilitate your development in Python by using a simplified MATLAB workflow and integrate your MATLAB code in Python. Simplifying data preparation using Image Labeler App from Python ..read more
Visit website

Follow mathinking on FeedSpot

Continue with Google
Continue with Apple
OR