結果:
Share your learning starting trouble experience of Matlab.. Looking forward for more answers..
Helllo all
I write The MATLAB Blog and have covered various enhancements to MATLAB's ODE capabilities over the last couple of years. Here are a few such posts
- The new solution framework for Ordinary Differential Equations (ODEs) in MATLAB R2023b
- Faster Ordinary Differential Equations (ODEs) solvers and Sensitivity Analysis of Parameters: Introducing SUNDIALS support in MATLAB
- Solving Higher-Order ODEs in MATLAB
- Function handles are faster in MATLAB R2023a (Faster function handles led to faster ode45 and friends)
- Understanding Tolerances in Ordinary Differential Equation Solvers
Everyone in this community has deeply engaged with all of these posts and given me lots of ideas for future enhancements which I've dutifully added to our internal enhancment request database.
Because I've asked for so much in this area, I was recently asked if there's anything else we should consider in the area of ODEs. Since all my best ideas come from all of you, I'm asking here....
So. If you could ask for new and improved functionality for solving ODEs with MATLAB, what would it be and (ideally) why?
Cheers,
Mike
Something that I periodically wonder about is whether an integration with the Rubi integration rules package would improve symbolic integration in Matlab's Symbolic Toolbox. The project is open-source with an MIT-licensed, has a Mathematica implementation, and supposedly SymPy is working on an implementation. Much of my intrigue comes from this 2022 report that compared the previous version of Rubi (4.16.1) against various CAS systems, including Matlab 2021a (Mupad):
While not really an official metric for Rubi, this does "feel" similar to my experience computing symbolic integrals in Matlab Symbolic Toolbox vs Maple/Mathematica. What do y'all think?
Yesterday I had an urgent service call for MatLab tech support. The Mathworks technician on call, Ivy Ngyuen, helped fix the problem. She was very patient and I truly appreciate her efforts, which resolved the issue. Thank you.
Check out how these charts were made with polar axes in the Graphics and App Building blog's latest article "Polar plots with patches and surface".
Nine new Image Processing courses plus one new learning path are now available as part of the Online Training Suite. These courses replace the content covered in the self-paced course Image Processing with MATLAB, which sunsets in 2026.
New courses include:
- Work with Image Data Types
- Image Registration
- Edge, Circle, and Line Detection
- Manage and Process Multiple Images
The new learning path Image Segmentation and Analysis in MATLAB earns users the digital credential Image Segmentation in MATLAB and contains the following courses:
Apparently, the back end here is running 2025b, hovering over the Run button and the Executing In popup both show R2024a.
ver matlab
all(logical.empty)
Discuss!
I just noticed that MATLAB R2025b is available. I am a bit surprised, as I never got notification of the beta test for it.
This topic is for highlights and experiences with R2025b.
For some time now, this has been bugging me - so I thought to gather some more feedback/information/opinions on this.
What would you classify Recursion? As a loop or as a vectorized section of code?
For context, this query occured to me while creating Cody problems involving strict (so to speak) vectorization - (Everyone is more than welcome to check my recent Cody questions).
To make problems interesting and/or difficult, I (and other posters) ban functions and functionalities - such as for loops, while loops, if-else statements, arrayfun() and the rest of the fun() family functions. However, some of the solutions including the reference solution I came up with for my latest problem, contained recursion.
I am rather divided on how to categorize it. What do you think?
Have you ever been enrolled in a course that uses an LMS and there is an assignment that invovles posting a question to, or answering a question in, a discussion group? This discussion group is meant to simulate that experience.
Independent researcher: Nguyễn Khánh Tùng
ORCID: 0009-0002-9877-4137
Email: traiphieu.com@gmail.com
Website: https://traiphieu.com
Theoretical Basis
The NKTg Law of Variable Inertia:
An object's tendency of motion in space depends on its position (x), velocity (v), and mass (m).
NKTg = f(x, v, m)
Fundamental interaction quantities:
NKTg1 = x * p
NKTg2 = (dm/dt) * p
where
p = m * v
For interpolation, we use:
m = NKTg1 / (x * v)
Research Objectives
- Verify interpolation of planetary masses using NKTg law.
- Compare with NASA real-time data (31/12/2024).
- Test sensitivity with Earth’s mass loss (NASA GRACE).
MATLAB Implementation
% NKTg Law Verification in MATLAB
% Author: Nguyen Khanh Tung
% Date: 31-12-2024
% Planetary data from NASA (30/12/2024)
planets = {
'Mercury','Venus','Earth','Mars','Jupiter','Saturn','Uranus','Neptune'};
x = [6.9817930e7, 1.08939e8, 1.471e8, 2.4923e8, ...
8.1662e8, 1.50653e9, 3.00139e9, 4.5589e9]; % km
v = [38.86, 35.02, 29.29, 24.07, 13.06, 9.69, 6.8, 5.43]; % km/s
m_nasa = [3.301e23, 4.867e24, 5.972e24, 6.417e23, ...
1.898e27, 5.683e26, 8.681e25, 1.024e26]; % kg
% Compute momentum
p = m_nasa .* v;
% Compute NKTg1
NKTg1 = x .* p;
% Interpolated masses using m = NKTg1 / (x*v)
m_interp = NKTg1 ./ (x .* v);
% Compare results in a table
T = table(planets', m_nasa', m_interp', (m_nasa - m_interp)', ...
'VariableNames', {'Planet','NASA_mass','Interpolated_mass','Delta_m'})
disp(T)
Results
- All 8 planets’ interpolated masses match NASA values almost perfectly.
- Deviation (Delta_m) ≈ 0 → error < 0.0001%.
- Confirms that NKTg1 is conserved across planetary orbits.
Earth’s Mass Loss (GRACE/GRACE-FO)
- GRACE missions show Earth loses mass annually (10^20 – 10^21 kg/year).
- NKTg interpolation detects Δm ≈ 3 × 10^19 kg.
- This matches the lower bound of NASA’s measured range.
Conclusion
- NKTg₁ interpolation is extremely accurate for planetary masses.
- Planetary data can be reconstructed with negligible error.
- NKTg model is sensitive enough to capture Earth’s small annual mass loss.
You can refer to the following four related articles to gain a deeper understanding of the NKTg Law and its applications
Variable Inertia From Abstract Concept to the Era of Measuring the ...
Inertia: A Millennia-Long Journey Inertia, the ability to maintain an object’s motion, has existed since ancient times. Aristotle viewed motion as an inherent property; an object stops only when t...
The functionality would allow report generation straight from live scripts that could be shared without exposing the code. This could be useful for cases where the recipient of the report only cares about the results and not the code details, or when the methodology is part of a company know how, e.g. Engineering services companies.
In order for it to be practical for use it would also require that variable values could be inserted into the text blocks, e.g. #var_name# would insert the value of the variable "var_name" and possibly selecting which code blocks to be hidden.
“Hello, I am Subha & I’m part of the organizing/mentoring team for NASA Space Apps Challenge Virudhunagar 2025 🚀. We’re looking for collaborators/mentors with ML and MATLAB expertise to help our student teams bring their space solutions to life. Would you be open to guiding us, even briefly? Your support could impact students tackling real NASA challenges. 🌍✨”
Independent researcher: Nguyễn Khánh Tùng
ORCID: 0009-0002-9877-4137
Email: traiphieu.com@gmail.com
Website: https://traiphieu.com
Hello everyone,
I would like to share some results from my recent research on the NKTg law of variable inertia and how it was experimentally verified using NASA JPL Horizons data (Dec 30–31, 2024).
🔹 What is the NKTg Law?
The law states that an object’s tendency of motion depends on the interaction between its position (x), velocity (v), and mass (m) through the conserved quantity:
NKTg1 = x * (m * v)
Here, m * v is the linear momentum.
If NKTg1 > 0 → the object tends to move away from equilibrium.
If NKTg1 < 0 → the object tends to return to equilibrium.
This law provides a new framework for analyzing orbital dynamics.
🔹 Research Objective
Interpolate the masses of all 8 planets using the NKTg law.
Compare results with NASA’s official planetary masses on 31/12/2024.
Test sensitivity for Earth’s mass loss as measured by GRACE / GRACE-FO missions.
🔹 Key Results
Table 1 – Mass Interpolation (31/12/2024)
Planet Interpolated Mass (kg) NASA Mass (kg) Δm Remarks
Mercury 3.301×10^23 3.301×10^23 ≈0 Perfect match
Venus 4.867×10^24 4.867×10^24 ≈0 Negligible error
Earth 5.972×10^24 5.972×10^24 ≈0 GRACE confirms slight variation
Mars 6.417×10^23 6.417×10^23 ≈0 Perfect match
Jupiter 1.898×10^27 1.898×10^27 ≈0 Stable mass
Saturn 5.683×10^26 5.683×10^26 ≈0 Error ≈ zero
Uranus 8.681×10^25 8.681×10^25 ≈0 Matches Voyager 2 data
Neptune 1.024×10^26 1.024×10^26 ≈0 Perfect match
Error rate: < 0.0001% across all planets.
🔹 Earth’s Mass Variation
NASA keeps Earth’s mass constant in official datasets.
GRACE/GRACE-FO show Earth loses ~10^20–10^21 kg annually (gas escape, ice melt, groundwater loss).
NKTg interpolation detected a slight decrease (~3 × 10^19 kg in 2024), which is within GRACE’s measured range.
This demonstrates the sensitivity of the NKTg model in detecting subtle real-world changes.
🔹 Why This Matters
Accuracy: NKTg interpolation perfectly matched NASA’s planetary masses.
Conservation: NKTg1 appears to be a conserved orbital quantity across both rocky and gas planets.
Applications:
- Real-time planetary mass estimation using (x, v) data.
- Integration into orbital mechanics simulations in MATLAB.
- Potential extensions into astrophysics and engineering models.
🔹 Conclusion
The NKTg law provides a novel way to interpolate planetary masses with extremely high accuracy, while also being sensitive to subtle physical changes like Earth’s gradual mass loss.
This could open up new opportunities for:
- Data-driven planetary modeling in MATLAB.
- Improved sensitivity in detecting small-scale variations not included in standard NASA datasets.
References:
- NASA JPL Horizons (planetary positions & velocities)
- NASA Planetary Fact Sheet (official masses)
- GRACE / GRACE-FO Mission Data (Earth mass loss)
I’d be very interested in hearing thoughts from the community about:
- How to integrate the NKTg model into MATLAB orbital simulations.
- Whether conserved quantities like NKTg1 could provide practical value beyond astronomy (e.g., physics simulations, engineering).
You can refer to the following four related articles to gain a deeper understanding of the NKTg Law and its applications
Best regards,
Nguyen Khanh Tung
Variable Inertia From Abstract Concept to the Era of Measuring the ...
Inertia: A Millennia-Long Journey Inertia, the ability to maintain an object’s motion, has existed since ancient times. Aristotle viewed motion as an inherent property; an object stops only when t...
For the www, uk, and in domains,a generative search answer is available for Help Center searches. Please let us know if you get good or bad results for your searches. Some have pointed out that it is not available in non-english domains. You can switch your country setting to try it out. You can also ask questions in different languages and ask for the response in a different language. I get better results when I ask more specific queries. How is it working for you?
I’d like to take a moment to highlight the great contributions of one of our community members, @Paul, who is fast approaching an impressive 5,000 reputation points!
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Modern engineering requires both robust hardware and powerful simulation tools. MATLAB and Simulink are widely used for data analysis, control design, and embedded system development. At the same time, Kasuo offers a wide range of components—from sensors and connectors to circuit protection devices—that engineers rely on to build real-world systems.
By combining these tools, developers can bridge the gap between simulation and implementation, ensuring their designs are reliable and ready for deployment.
Example Use Case: Sensor Data Acquisition and Processing
- Kasuo Hardware Setup
- Select a Kasuo sensor (e.g., temperature, microphone, or motion sensor).
- Connect it to a DAQ or microcontroller board for data collection.
- Data Acquisition in MATLAB
- Use MATLAB’s Data Acquisition Toolbox to stream sensor data directly.
- Example snippet:
s = daq("ni");
addinput(s,
"Dev1", "ai0", "Voltage");
data = read(s, seconds(
5), "OutputFormat", "Matrix");
plot(data);
- Signal Processing with Simulink
- Build a Simulink model to filter noise, detect anomalies, or design control logic.
- Simulink enables real-time visualization and iterative tuning.
- Validation & Protection Simulation
- Add Kasuo’s circuit protection components (e.g., TVS diodes, surge suppressors) in the physical design.
- Use Simulink to simulate stress conditions, validating system robustness before hardware testing.
Benefits of the Workflow
- Faster prototyping with MATLAB & Simulink.
- Greater reliability by incorporating Kasuo protection devices.
- Seamless transition from model to hardware implementation.
Conclusion
Kasuo’s electronic components provide the hardware foundation for many embedded and signal processing applications. When combined with MATLAB and Simulink, engineers can design, simulate, and validate systems more efficiently—reducing risks and development time.
With AI agents dev coding on other languages has become so easy.
Im waiting for matlab to build something like warp but for matlab.
I know they have the current ai but with all respect it's rubbish compared to vibe coding tools in others sectors.
Matlab leads AI so it really should be leading this space.
Hello MATLAB Central community,
My name is Yann. And I love MATLAB. I also love Python ... 🐍 (I know, not the place for that).
I recently decided to go down the rabbit hole of AI. So I started benchmarking deep learning frameworks on basic examples. Here is a recording of my experiment:
Happy to engage in the debate. What do you think?
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