Updated Discussions

Large Language Models (LLMs) with MATLAB was updated again today to support the newly released OpenAI models GPT-5, GPT-5 mini, GPT-5 nano, GPT-5 chat, o3, and o4-mini. When you create an openAIChat object, set the ModelName name-value argument to "gpt-5", "gpt-5-mini", "gpt-5-nano", "gpt-5-chat-latest", "o4-mini", or "o3".
This is version 4.4.0 of this free MATLAB add-on that lets you interact with LLMs on MATLAB. The release notes are at Release v4.4.0: Support for GPT-5, o3, o4-mini · matlab-deep-learning/llms-with-matlab
I used it recently to play with the new open-weight models released by OpenAI.
Here is a screenshot of a Cody problem that I just created. The math rendering is poor. (I have since edited the problem to remove the math formatting.)
Are there any code restrictions for programming Cody solutions? I could not find anything mentioned at https://www.mathworks.com/matlabcentral/content/cody/about.html, other than toolbox functions not being available.
I'm seeing solution maps shown with low-contrast gray colors instead of the correct symbol colors. I have observed this using both Safari and Chrome. Screenshot:
Hey everyone,
I’m currently working with MATLAB R2025b and using the MQTT blocks from the Industrial Communication Toolbox inside Simulink. I’ve run into an issue that’s driving me a bit crazy, and I’m not sure if it’s a bug or if I’m missing something obvious.
Here’s what’s happening:
  • I open the MQTT Configure block.
  • I fill out all the required fields — Broker address, Port, Client ID, Username, and Password.
  • When I click Test Connection, it says “Connection established successfully.” So far so good.
  • Then I click Apply, close the dialog, set the topic name, and try to run the simulation.
  • At this point, I get the following error:Caused by: Invalid value for 'ClientID', 'Username' or 'Password'.
  • When I reopen the MQTT config block, I notice that the Password field is empty again — even though I definitely entered it before and the connection test worked earlier.
It seems like Simulink is somehow not saving the password after hitting Apply, which leads to the authentication error during simulation.
Has anyone else faced this? Is this a bug in R2025b, or do I need to configure something differently to make the password persist?
Would really appreciate any insights, workarounds, or confirmations from anyone who has used MQTT in Simulink recently.
Thanks in advance!
Get ready to roll up your sleeves at MATLAB EXPO 2025 – our global online event is back, and this year we’re offering 10 hands-on workshops designed to spark innovation and deepen your skills with MATLAB Online and Simulink Online.
Whether you're exploring AI, modeling batteries, or building carbon trackers, these live workshops are your chance to:
  • Work directly in MATLAB and Simulink Online
  • Solve real-world challenges with guidance from MathWorks experts
  • Connect with peers across industries
  • Ask questions and get live feedback
Join the Experience to learn more about each workshop below!
Which workshop are you most excited to attend?!
Day 1:
  • Beyond the Labels: Leveraging AI Techniques for Enlightened Product Choices
  • A Hands-On Introduction to Reinforcement Learning with MATLAB and Simulink
  • Curriculum Development with MATLAB Copilot and Generative AI
  • Simscape Battery Workshop
  • Generating Tests for your MATLAB code
Day 2:
  • Hands-On AI for Smart Appliances: From Sensor Data to Embedded Code
  • A Hands-On Introduction to Reduced Order Modeling with MATLAB and Simulink
  • Introduction to Research Software and Development with Simulink
  • Hack Your Carbon Impact: Build and Publish an Emissions Tracker with MATLAB
  • How to Simulate Scalable Cellular and Connectivity Networks: A Hands-On Session
We look forward to Accelerating the Pace of Engineering and Science together!
Mary Ann Freeman
Mary Ann Freeman
Last activity on 21 Oct 2025 at 14:34

AI Is Transforming Engineering Workflows

It’s an honor to deliver the keynote at MATLAB EXPO 2025. I'll explore how AI changes the game in engineered systems, bringing intelligence to every step of the process from design to deployment. This short video captures a glimpse of what I’ll share:
https://www.youtube.com/watch?v=90JOxCheurk
What excites or challenges you about this shift? Drop a comment or start a thread!
Show Results
David
David
Last activity on 20 Oct 2025 at 21:26

Shortcut to MATLAB Online - matlab.new

I just learned you can access MATLAB Online from the following shortcut in your web browser: https://matlab.new
Thanks @Yann Debray
From his recent blog post: pip & uv in MATLAB Online » Artificial Intelligence - MATLAB & Simulink
I'm developing a comprehensive MATLAB programming course and seeking passionate co-trainers to collaborate!
Why MATLAB Matters:Many people underestimate MATLAB's significance in:
  • Communication systems
  • Signal processing
  • Mathematical modeling
  • Engineering applications
  • Scientific computing
Course Structure:
  1. Foundation Module: MATLAB basics and fundamentals
  2. Image Processing: Practical applications and techniques
  3. Signal Processing: Analysis and implementation
  4. Machine Learning: ML algorithms using MATLAB
  5. Hands-on Learning: Projects, assignments.
What I'm Looking For:
  • Enthusiastic educators willing to share knowledge
  • Experience in any MATLAB application area
  • Commitment to collaborative teaching
Interested in joining as a co-trainer? Please comment below or reach out directly!
Excited for MATLAB EXPO 2025!
I’m a Master’s student in Electrical Engineering at UNSW Sydney, researching EV fleet charging and hybrid energy strategies integrating battery-electric and hydrogen fuel cell vehicles.
LinkedIn link: www.linkedin.com/in/yuanzhe-chen-6b2158351
ResearchGate link: https://www.researchgate.net/profile/Yuanzhe-Chen-9?ev=hdr_xprf
#MATLABEXPO #EV #FCEV #SmartGrid
I'm working on training neural networks without backpropagation / automatic differentiation, using locally derived analytic forms of update rules. Given that this allows a direct formula to be derived for the update rule, it removes alot of the overhead that is otherwise required from automatic differentiation.
However, matlab's functionalities for neural networks are currently solely based around backpropagation and automatic differentiation, such as the dlgradient function and requiring everything to be dlarrays during training.
I have two main requests, specifically for functions that perform a single operation within a single layer of a neural network, such as "dlconv", "fullyconnect", "maxpool", "avgpool", "relu", etc:
  • these functions should also allow normal gpuArray data instead of requiring everything to be dlarrays.
  • these functions are currently designed to only perform the forward pass. I request that these also be designed to perform the backward pass if user requests. There can be another input user flag that can be "forward" (default) or "backward", and then the function should have all the necessary inputs to perform that operation (e.g. for "avgpool" forward pass it only needs the avgpool input data and the avgpool parameters, but for the "avgpool" backward pass it needs the deriviative w.r.t. the avgpool output data, the avgpool parameters, and the original data dimensions). I know that there is a maxunpool function that achieves this for maxpool, but it has significant issues when trying to use it this way instead of by backpropagation in a dlgradient type layer, see (https://www.mathworks.com/matlabcentral/answers/2179587-making-a-custom-way-to-train-cnns-and-i-am-noticing-that-avgpool-is-significantly-faster-than-maxpo?s_tid=srchtitle).
I don't know how many people would benefit from this feature, and someone could always spend their time creating these functionalities themselves by matlab scripts, cuDNN mex, etc., but regardless it would be nice for matlab to have this allowable for more customizable neural net training.
I don't like the change
16%
I really don't like the change
29%
I'm okay with the change
24%
I love the change
11%
I'm indifferent
11%
I want both the web & help browser
11%
38 votes
Patrick
Patrick
Last activity on 16 Oct 2025 at 17:15

hi scholars

happy to be here
Edit 15 Oct 2025: Removed incorrect code. Replaced symmatrix2sym and symfunmatrix2symfun with sym and symfun respectively (latter supported as of 2024b).
The Symbolic Math Toolbox does not have its own dot and and cross functions. That's o.k. (maybe) for garden variety vectors of sym objects where those operations get shipped off to the base Matlab functions
x = sym('x',[3,1]); y = sym('y',[3,1]);
which dot(x,y)
/MATLAB/toolbox/matlab/specfun/dot.m
dot(x,y)
ans = 
which cross(x,y)
/MATLAB/toolbox/matlab/specfun/cross.m
cross(x,y)
ans = 
But now we have symmatrix et. al., and things don't work as nicely
clearvars
x = symmatrix('x',[3,1]); y = symmatrix('y',[3,1]);
z = symmatrix('z',[1,1]);
sympref('AbbreviateOutput',false);
dot() expands the result, which isn't really desirable for exposition.
eqn = z == dot(x,y)
eqn = 
Also, dot() returns the the result in terms of the conjugate of x, which can't be simplifed away at the symmatrix level
assumeAlso(sym(x),'real')
class(eqn)
ans = 'symmatrix'
try
eqn = z == simplify(dot(x,y))
catch ME
ME.message
end
ans = 'Undefined function 'simplify' for input arguments of type 'symmatrix'.'
To get rid of the conjugate, we have to resort to sym
eqn = simplify(sym(eqn))
eqn = 
but again we are in expanded form, which defeats the purpose of symmatrix (et. al.)
But at least we can do this to get a nice equation
eqn = z == x.'*y
eqn = 
dot errors with symfunmatrix inputs
clearvars
syms t real
x = symfunmatrix('x(t)',t,[3,1]); y = symfunmatrix('y(t)',t,[3,1]);
try
dot(x,y)
catch ME
ME.message
end
ans = 'Invalid argument at position 2. Symbolic function is evaluated at the input arguments and does not accept colon indexing. Instead, use FORMULA on the function and perform colon indexing on the returned output.'
Cross works (accidentally IMO) with symmatrix, but expands the result, which isn't really desirable for exposition
clearvars
x = symmatrix('x',[3,1]); y = symmatrix('y',[3,1]);
z = symmatrix('z',[3,1]);
eqn = z == cross(x,y)
eqn = 
And it doesn't work at all if an input is a symfunmatrix
syms t
w = symfunmatrix('w(t)',t,[3,1]);
try
eqn = z == cross(x,w);
catch ME
ME.message
end
ans = 'A and B must be of length 3 in the dimension in which the cross product is taken.'
In the latter case we can expand with
eqn = z == cross(sym(x),symfun(w)) % x has to be converted
eqn(t) = 
But we can't do the same with dot (as shown above, dot doesn't like symfun inputs)
try
eqn = z == dot(sym(x),symfun(w))
catch ME
ME.message
end
ans = 'Invalid argument at position 2. Symbolic function is evaluated at the input arguments and does not accept colon indexing. Instead, use FORMULA on the function and perform colon indexing on the returned output.'
Looks like the only choice for dot with symfunmatrix is to write it by hand at the matrix level
x.'*w
ans(t) = 
or at the sym/symfun level
sym(x).'*symfun(w) % assuming x is real
ans(t) = 
Ideally, I'd like to see dot and cross implemented for symmatrix and symfunmatrix types where neither function would evaluate, i.e., expand, until both arguments are subs-ed with sym or symfun objects of appropriate dimension.
Also, it would be nice if symmatrix could be assumed to be real. Is there a reason why being able to do so wouldn't make sense?
try
assume(x,'real')
catch ME
ME.message
end
ans = 'Undefined function 'assume' for input arguments of type 'symmatrix'.'
Automating Parameter Identifiability Analysis in SimBiology
Is it possible to develop a MATLAB Live Script that automates a series of SimBiology model fits to obtain likelihood profiles? The goal is to fit a kinetic model to experimental data while systematically fixing the value of one kinetic constant (e.g., k1) and leaving the others unrestricted.
The script would perform the following:
Use a pre-configured SimBiology project where the best fit to the experimental data has already been established (including dependent/independent variables, covariates, the error model, and optimization settings).
Iterate over a defined sequence of fixed values for a chosen parameter.
For each fixed value, run the estimation to optimize the remaining parameters.
Record the resulting Sum of Squared Errors (SSE) for each run.
The final output would be a likelihood profile—a plot of SSE versus the fixed parameter value (e.g., k1)—to assess the practical identifiability of each model parameter.

About Discussions

Discussions is a user-focused forum for the conversations that happen outside of any particular product or project.

Get to know your peers while sharing all the tricks you've learned, ideas you've had, or even your latest vacation photos. Discussions is where MATLAB users connect!

More Community Areas

MATLAB Answers

Ask & Answer questions about MATLAB & Simulink!

File Exchange

Download or contribute user-submitted code!

Cody

Solve problem groups, learn MATLAB & earn badges!

Blogs

Get the inside view on MATLAB and Simulink!

AI Chat Playground

Use AI to generate initial draft MATLAB code, and answer questions!