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The AI Chat Playground at MATLAB Central has two new upgrades: OpenAI GPT-4o mini and MATLAB R2024b!
GPT-4o mini is a new language model from OpenAI and brings general knowledge up to October 2023. GPT-4o mini surpasses GPT-3.5 Turbo and other small models on academic benchmarks across both textual intelligence and reasoning. Our goal is to keep improving the output of the AI Chat Playground. This upgrade is available now: https://www.mathworks.com/matlabcentral/playground/
One more thing... we also updated the system to the latest release of MATLAB. This is R2024b and comes with hundreds of updates and new plot types to explore.Check out Mike Croucher's blog post about the latest version of MATLAB: https://blogs.mathworks.com/matlab/2024/09/13/the-latest-version-of-matlab-r2024b-has-just-been-released/
We are looking forward to your feedback on the updates to the AI Chat Playground. Let us know what you think and how you use this community app.
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Always!
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Hello everyone,
Over the past few weeks, our community members have shared some incredible insights and resources. Here are some highlights worth checking out:
Interesting Questions
Johnathan is seeking help with implementing a complex equation into MATLAB's curve fitting toolbox. If you have experience with curve fitting or MATLAB, your input could be invaluable!
Popular Discussions
Athanasios continues his exploration of the Duffing Equation, delving into its chaotic behavior. It's a fascinating read for anyone interested in nonlinear dynamics or chaos theory.
John shares his playful exploration with MATLAB to find a generative equation for a sequence involving Fibonacci numbers. It's an intriguing challenge for those who love mathematical puzzles.
From File Exchange
Ayesha provides a graphical analysis of linearised models in epidemiology, offering a detailed look at the dynamics of these systems. This resource is perfect for those interested in mathematical modeling.
Gareth brings some humor to MATLAB with a toolbox designed to share jokes. It's a fun way to lighten the mood during conferences or meetups.
From the Blogs
Ned Gulley interviews Tim Marston, the 2023 MATLAB Mini Hack contest winner. Tim's creativity and skills are truly inspiring, and his story is a must-read for aspiring programmers.
Sivylla discusses the integration of AI with embedded systems, highlighting the benefits of using MATLAB and Simulink. It's an insightful read for anyone interested in the future of AI technology.
Thank you to all our contributors for sharing your knowledge and creativity. We encourage everyone to engage with these posts and continue fostering a vibrant and supportive community.
Happy exploring!
Explore the newest online training courses, available as of 2024b: one new Onramp, eight new short courses, and one new learning path. Yes, that’s 10 new offerings. We’ve been busy.
As a reminder, Onramps are free to all. Short courses and learning paths require a subscription to the Online Training Suite (OTS).
- Multibody Simulation Onramp
- Analyzing Results in Simulink
- Battery Pack Modeling
- Introduction to Motor Control
- Signal Processing Techniques for Streaming Signals
- Core Signal Processing Techniques in MATLAB (learning path – includes the four short courses listed below)
Local large language models (LLMs), such as llama, phi3, and mistral, are now available in the Large Language Models (LLMs) with MATLAB repository through Ollama™!
Read about it here:
Hot off the heels of my High Performance Computing experience in the Czech republic, I've just booked my flights to Atlanta for this year's supercomputing conference at SC24.
Will any of you be there?
syms u v
atan2alt(v,u)
function Z = atan2alt(V,U)
% extension of atan2(V,U) into the complex plane
Z = -1i*log((U+1i*V)./sqrt(U.^2+V.^2));
% check for purely real input. if so, zero out the imaginary part.
realInputs = (imag(U) == 0) & (imag(V) == 0);
Z(realInputs) = real(Z(realInputs));
end
As I am editing this post, I see the expected symbolic display in the nice form as have grown to love. However, when I save the post, it does not display. (In fact, it shows up here in the discussions post.) This seems to be a new problem, as I have not seen that failure mode in the past.
You can see the problem in this Answer forum response of mine, where it did fail.
In case you haven't come across it yet, @Gareth created a Jokes toolbox to get MATLAB to tell you a joke.
I was browsing the MathWorks website and decided to check the Cody leaderboard. To my surprise, William has now solved 5,000 problems. At the moment, there are 5,227 problems on Cody, so William has solved over 95%. The next competitor is over 500 problems behind. His score is also clearly the highest, approaching 60,000.
Has this been eliminated? I've been at 31 or 32 for 30 days for awhile, but no badge. 10 badge was automatic.
Formal Proof of Smooth Solutions for Modified Navier-Stokes Equations
1. Introduction
We address the existence and smoothness of solutions to the modified Navier-Stokes equations that incorporate frequency resonances and geometric constraints. Our goal is to prove that these modifications prevent singularities, leading to smooth solutions.
2. Mathematical Formulation
2.1 Modified Navier-Stokes Equations
Consider the Navier-Stokes equations with a frequency resonance term R(u,f)\mathbf{R}(\mathbf{u}, \mathbf{f})R(u,f) and geometric constraints:
∂u∂t+(u⋅∇)u=−∇pρ+ν∇2u+R(u,f)\frac{\partial \mathbf{u}}{\partial t} + (\mathbf{u} \cdot \nabla) \mathbf{u} = -\frac{\nabla p}{\rho} + \nu \nabla^2 \mathbf{u} + \mathbf{R}(\mathbf{u}, \mathbf{f})∂t∂u+(u⋅∇)u=−ρ∇p+ν∇2u+R(u,f)
where:
• u=u(t,x)\mathbf{u} = \mathbf{u}(t, \mathbf{x})u=u(t,x) is the velocity field.
• p=p(t,x)p = p(t, \mathbf{x})p=p(t,x) is the pressure field.
• ν\nuν is the kinematic viscosity.
• R(u,f)\mathbf{R}(\mathbf{u}, \mathbf{f})R(u,f) represents the frequency resonance effects.
• f\mathbf{f}f denotes external forces.
2.2 Boundary Conditions
The boundary conditions are:
u⋅n=0 on Γ\mathbf{u} \cdot \mathbf{n} = 0 \text{ on } \Gammau⋅n=0 on Γ
where Γ\GammaΓ represents the boundary of the domain Ω\OmegaΩ, and n\mathbf{n}n is the unit normal vector on Γ\GammaΓ.
3. Existence and Smoothness of Solutions
3.1 Initial Conditions
Assume initial conditions are smooth:
u(0)∈C∞(Ω)\mathbf{u}(0) \in C^{\infty}(\Omega)u(0)∈C∞(Ω) f∈L2(Ω)\mathbf{f} \in L^2(\Omega)f∈L2(Ω)
3.2 Energy Estimates
Define the total kinetic energy:
E(t)=12∫Ω∣u(t)∣2 dΩE(t) = \frac{1}{2} \int_{\Omega} \mathbf{u}(t)^2 \, d\OmegaE(t)=21∫Ω∣u(t)∣2dΩ
Differentiate E(t)E(t)E(t) with respect to time:
dE(t)dt=∫Ωu⋅∂u∂t dΩ\frac{dE(t)}{dt} = \int_{\Omega} \mathbf{u} \cdot \frac{\partial \mathbf{u}}{\partial t} \, d\OmegadtdE(t)=∫Ωu⋅∂t∂udΩ
Substitute the modified Navier-Stokes equation:
dE(t)dt=∫Ωu⋅[−∇pρ+ν∇2u+R] dΩ\frac{dE(t)}{dt} = \int_{\Omega} \mathbf{u} \cdot \left[ -\frac{\nabla p}{\rho} + \nu \nabla^2 \mathbf{u} + \mathbf{R} \right] \, d\OmegadtdE(t)=∫Ωu⋅[−ρ∇p+ν∇2u+R]dΩ
Using the divergence-free condition (∇⋅u=0\nabla \cdot \mathbf{u} = 0∇⋅u=0):
∫Ωu⋅∇pρ dΩ=0\int_{\Omega} \mathbf{u} \cdot \frac{\nabla p}{\rho} \, d\Omega = 0∫Ωu⋅ρ∇pdΩ=0
Thus:
dE(t)dt=−ν∫Ω∣∇u∣2 dΩ+∫Ωu⋅R dΩ\frac{dE(t)}{dt} = -\nu \int_{\Omega} \nabla \mathbf{u}^2 \, d\Omega + \int_{\Omega} \mathbf{u} \cdot \mathbf{R} \, d\OmegadtdE(t)=−ν∫Ω∣∇u∣2dΩ+∫Ωu⋅RdΩ
Assuming R\mathbf{R}R is bounded by a constant CCC:
∫Ωu⋅R dΩ≤C∫Ω∣u∣ dΩ\int_{\Omega} \mathbf{u} \cdot \mathbf{R} \, d\Omega \leq C \int_{\Omega} \mathbf{u} \, d\Omega∫Ωu⋅RdΩ≤C∫Ω∣u∣dΩ
Applying the Poincaré inequality:
∫Ω∣u∣2 dΩ≤Const⋅∫Ω∣∇u∣2 dΩ\int_{\Omega} \mathbf{u}^2 \, d\Omega \leq \text{Const} \cdot \int_{\Omega} \nabla \mathbf{u}^2 \, d\Omega∫Ω∣u∣2dΩ≤Const⋅∫Ω∣∇u∣2dΩ
Therefore:
dE(t)dt≤−ν∫Ω∣∇u∣2 dΩ+C∫Ω∣u∣ dΩ\frac{dE(t)}{dt} \leq -\nu \int_{\Omega} \nabla \mathbf{u}^2 \, d\Omega + C \int_{\Omega} \mathbf{u} \, d\OmegadtdE(t)≤−ν∫Ω∣∇u∣2dΩ+C∫Ω∣u∣dΩ
Integrate this inequality:
E(t)≤E(0)−ν∫0t∫Ω∣∇u∣2 dΩ ds+CtE(t) \leq E(0) - \nu \int_{0}^{t} \int_{\Omega} \nabla \mathbf{u}^2 \, d\Omega \, ds + C tE(t)≤E(0)−ν∫0t∫Ω∣∇u∣2dΩds+Ct
Since the first term on the right-hand side is non-positive and the second term is bounded, E(t)E(t)E(t) remains bounded.
3.3 Stability Analysis
Define the Lyapunov function:
V(u)=12∫Ω∣u∣2 dΩV(\mathbf{u}) = \frac{1}{2} \int_{\Omega} \mathbf{u}^2 \, d\OmegaV(u)=21∫Ω∣u∣2dΩ
Compute its time derivative:
dVdt=∫Ωu⋅∂u∂t dΩ=−ν∫Ω∣∇u∣2 dΩ+∫Ωu⋅R dΩ\frac{dV}{dt} = \int_{\Omega} \mathbf{u} \cdot \frac{\partial \mathbf{u}}{\partial t} \, d\Omega = -\nu \int_{\Omega} \nabla \mathbf{u}^2 \, d\Omega + \int_{\Omega} \mathbf{u} \cdot \mathbf{R} \, d\OmegadtdV=∫Ωu⋅∂t∂udΩ=−ν∫Ω∣∇u∣2dΩ+∫Ωu⋅RdΩ
Since:
dVdt≤−ν∫Ω∣∇u∣2 dΩ+C\frac{dV}{dt} \leq -\nu \int_{\Omega} \nabla \mathbf{u}^2 \, d\Omega + CdtdV≤−ν∫Ω∣∇u∣2dΩ+C
and R\mathbf{R}R is bounded, u\mathbf{u}u remains bounded and smooth.
3.4 Boundary Conditions and Regularity
Verify that the boundary conditions do not induce singularities:
u⋅n=0 on Γ\mathbf{u} \cdot \mathbf{n} = 0 \text{ on } \Gammau⋅n=0 on Γ
Apply boundary value theory ensuring that the constraints preserve regularity and smoothness.
4. Extended Simulations and Experimental Validation
4.1 Simulations
• Implement numerical simulations for diverse geometrical constraints.
• Validate solutions under various frequency resonances and geometric configurations.
4.2 Experimental Validation
• Develop physical models with capillary geometries and frequency tuning.
• Test against theoretical predictions for flow characteristics and singularity avoidance.
4.3 Validation Metrics
Ensure:
• Solution smoothness and stability.
• Accurate representation of frequency and geometric effects.
• No emergence of singularities or discontinuities.
5. Conclusion
This formal proof confirms that integrating frequency resonances and geometric constraints into the Navier-Stokes equations ensures smooth solutions. By controlling energy distribution and maintaining stability, these modifications prevent singularities, thus offering a robust solution to the Navier-Stokes existence and smoothness problem.
I've been working on some matrix problems recently(Problem 55225)
and this is my code
It turns out that "Undefined function 'corr' for input arguments of type 'double'." However, should't the input argument of "corr" be column vectors with single/double values? What's even going on there?
So generally I want to be using uifigures over figures. For example I really like the tab group component, which can really help with organizing large numbers of plots in a manageable way. I also really prefer the look of the progress dialog, uialert, confirm, etc. That said, I run into way more bugs using uifigures. I always get a “flicker” in the axes toolbar for example. I also have matlab getting “hung” a lot more often when using uifigures.
So in general, what is recommended? Are uifigures ever going to fully replace traditional figures? Are they going to become more and more robust? Do I need a better GPU to handle graphics better? Just looking for general guidance.
Hi everyone, I am from India ..Suggest some drone for deploying code from Matlab.
Hello :-) I am interested in reading the book "The finite element method for solid and structural mechanics" online with somebody who is also interested in studying the finite element method particularly its mathematical aspect. I enjoy discussing the book instead of reading it alone. Please if you were interested email me at: student.z.k@hotmail.com Thank you!
Imagine that the earth is a perfect sphere with a radius of 6371000 meters and there is a rope tightly wrapped around the equator. With one line of MATLAB code determine how much the rope will be lifted above the surface if you cut it and insert a 1 meter segment of rope into it (and then expand the whole rope back into a circle again, of course).
Swimming, diving
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4%
Gymnastics
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Other indoor arena sport
15%
track, field
24%
Other outdoor sport
21%
346 voti
Hello, MATLAB enthusiasts! 🌟
Over the past few weeks, our community has been buzzing with insightful questions, vibrant discussions, and innovative ideas. Whether you're a seasoned expert or a curious beginner, there's something here for everyone to learn and enjoy. Let's take a moment to highlight some of the standout contributions that have sparked interest and inspired many. Dive in and see how you can join the conversation or find solutions to your own challenges!
Interesting Questions
How can i edit my code which works on r2014b version at work but not on my personal r2024a version? by Oluwadamilola Oke
Oluwadamilola Oke is seeking assistance with a MATLAB code that works on version r2014b but encounters errors on version r2024a. The issue seems to be related to file location or the use of specific commands like movefile. If you have experience with these versions of MATLAB, your expertise could be invaluable.
Yohay has been working on a simulation to measure particle speed and fit it to the Maxwell-Boltzmann distribution. However, the fit isn't aligning perfectly with the data. Yohay has shared the code and histogram data for community members to review and provide suggestions.
Alessandro Livi is toggling between C++ for Arduino Pico and MATLAB App Designer. They suggest an enhancement where typing // for comments in MATLAB automatically converts to %. This small feature could improve the workflow for many users who switch between programming languages.
Popular Discussions
Athanasios Paraskevopoulos has started an engaging discussion on Gabriel's Horn, a shape with infinite surface area but finite volume. The conversation delves into the mathematical intricacies and integral calculations required to understand this paradoxical shape.
Honzik has brought up an interesting topic about custom fonts for MATLAB. While popular coding fonts handle characters like 0 and O well, they often fail to distinguish between different types of brackets. Honzik suggests that MathWorks could develop a custom font optimized for MATLAB syntax to reduce coding errors.
From the Blogs
Guy Rouleau addresses a common error in Simulink models: "Derivative of state '1' in block 'X/Y/Integrator' at time 0.55 is not finite." The blog post explores various tools and methods to diagnose and resolve this issue, making it a valuable read for anyone facing similar challenges.
Guest writer Gianluca Carnielli, featured by Adam Danz, shares insights on creating time-sensitive animations using MATLAB. The article covers controlling the motion of multiple animated objects, organizing data with timetables, and simplifying animations with the retime function. This is a must-read for anyone interested in scientific animations.
Feel free to check out these fascinating contributions and join the discussions! Your input and expertise can make a significant difference in our community.
hello i found the following tools helpful to write matlab programs. copilot.microsoft.com chatgpt.com/gpts gemini.google.com and ai.meta.com. thanks a lot and best wishes.