MaplePrimes - Maplesoft Blog

Build, Explore, Optimize, Connect: What’s New in MapleSim 2026

svariath — Tue, 03 Nov 2026 19:58:10 Z

Ask any engineer where the time goes in a modeling project, and the answer is rarely "the math."

It's everything around it: hunting for commands, opening help in another window, checking a plot, finding your place in the model again. Each interruption is small. Together, they can turn an afternoon of modeling into a week.

MapleSim 2026 was built around that idea. From the first install to the final result, it keeps what you need right where you're working.

Getting started is now simpler than ever. MapleSim, all its toolboxes and the Maple engine are now delivered in a single installer, so one installation provides everything needed to get started.

Context-sensitive hover bars display the most frequently used commands next to the current selection, and consolidated menus group related commands together. These changes reduce the steps required to build and modify models.

A new split pane, which can be arranged beside the diagram or opened in a separate window, updates with each selection to display the component's help page, its Modelica code, or live plots of its variables in the time or frequency domain. Engineers can review component details without leaving the model.

The Optimization app now displays live system plots and a time history of the objective function during execution, allowing engineers to assess convergence and intervene early when needed. The in-product apps have also been redesigned with a more compact layout for use alongside the split pane.

FMI import and export now support string and discrete parameters, inputs, and outputs. The MapleSim CAD Toolbox supports the latest formats from Catia® V5, SOLIDWORKS®, Creo®, NX™, Inventor®, Solid Edge®, Parasolid®, and other CAD tools. MapleSim 2026 also supports the latest Maple interface, including ribbons, the AI Assistant, and improved performance.

Building a web line model involves many components, each with its own connections and parameters. The new Web Handling Model Creation App automates this process. Starting from a tabulated data file describing the line, the app validates the data, calculates wrap angles, span lengths, and roller mass and inertia, and generates the complete MapleSim model. Engineers can then proceed directly to adding drives, dancers, and control loops to analyze line performance.

New components extend modeling coverage to more of the processes found on real lines:

Noncircular winding and unwinding, for rolls that are not perfectly round
Delamination, where one web separates into two
Web-on-web friction, for two webs running through a nip roller together
Tendency drives, using the new Roller Shaft component

The library is also easier to use and delivers more accurate results. A reorganized Rollers sub-palette groups components into Rollers, Nip Rollers, Multi-Web, and Parameters. Sequential and S-Wrap Nip Rollers simplify S-wrap configurations, and a per-unit option adds flexibility to drives and controls. Improved handling of span tension loss and special nip roller configurations further increases accuracy.

MapleSim 2026 adds new components that capture more real-world detail in machine models.

Ropes and Pulleys Library: Variable Winch 3D allows engineers to define the shape of the winch drum. Pulley Edge models side contact between the sheave and the rope, and Pulley Multi-Turn models multiple rope turns for added friction, complete with visualization.
Multibody Library: A new Gear Forces sub-package provides simplified models for spur, helical, bevel, and crossed gear pairs, along with a ratchet wheel model.
Directional Control Valve app: The app now creates pneumatic valves in addition to hydraulic valves.

MapleSim 2026 reduces the time spent navigating the software and increases the time available for what matters most: understanding and improving designs.

To try MapleSim 2026, start your free instant evaluation today. For a complete overview of the release, visit What's New in MapleSim 2026.

What's next for the Maple Conference

pchin — Tue, 13 Oct 2026 16:19:29 Z

For those who are familiar with the long history of the Maple Conference, you’ll know that we vary the format from time to time, and for the next conference, we'll be trying something new!

The next event will be a user conference, strongly focused on enabling Maple users to get the most out of Maple, increasing their knowledge and abilities through a series of technical talks by Maple experts. We’ll release more details later, but our main goal is to engage with and support the many customers who love Maple and want to learn how to do even more with it. This event will be held in the spring of 2027, which we hope will be a convenient time for those who find it difficult to attend a fall event.

Another aspect of past conferences has been the opportunity for users to share their work with the Maple community. We'd like to remind you that the Maple Transactions, an open-access online journal, is an ideal place to publish your work, and you can see an overview of the journal in this short video. If you prefer a less formal venue, we encourage you to post right here on MaplePrimes to share your work and interact with the community.

We will share more details as we get closer to the event. For now, we just wanted to give you an idea of the plans, as we know that many people look forward to the Maple Conference and expect to hear from us around this time.

Calling All Instructors: Introducing Maple Classroom

Karishma — Fri, 28 Aug 2026 15:59:01 Z

If you teach with Maple, this one's for you.

You already know Maple as a place where students can explore, visualize, experiment, and work through problems in ways a static worksheet or textbook can't match. Maybe you've built worksheets, explorations, or activities you use year after year. What you haven't had, until now, is an easy way to assign that work directly to your class and see how students are actually engaging with it while they work.

That's what Maple Classroom is for.

In the age of AI, students can produce polished, correct-looking work more easily than ever. A right answer doesn't tell you much about how a student got there, where they got stuck, or whether they're actually building understanding. The goal with Maple Classroom is that now you can see how students are approaching a problem — not just what they submitted at the end.

Maple Classroom lets you create, assign, observe, and respond, without ever leaving Maple. You can create a class and enroll students with a class code, then assign activities for use in class or independently outside it.

Imagine doing a quick poll in class, or assigning a reasoning task as practice, and seeing all the different approaches students took to solve it — not just who got the right answer, but how they got there, where they got stuck, and what that tells you about where the class stands.

You can even define a rubric that tells the AI Assistant what to look for, and let it summarize how students approached an activity and where they landed.

Maple Classroom isn't an LMS or a grading system. And you don't need to rebuild your course materials to use it — it's an added layer on the Maple experience you already teach with, giving you visibility you didn't have before.

Maple Classroom is available now as a Technology Preview for Maple 2026. If you're teaching with Maple, give Maple Classroom a try with your students and let us know what you think — your feedback will help shape what it becomes.

Maple Classroom requires Maple 2026 and an active Elite Maintenance Program (EMP) subscription.

Finding the Shortest Path Between Two Points

callumneily — Thu, 20 Aug 2026 19:57:07 Z

Suppose you place two points on the x-y plane and ask a seemingly simple question: What is the shortest path connecting them?

The "obvious" answer is a straight line (the green curve), but how can we show that this is truly the optimal path? Imagine two fixed points (x₁,y₁) and (x₂,y₂) connected by some curve y(x).

To find the length of this curve, we zoom in on an infinitesimally small segment. If the curve changes by small amounts dx horizontally and dy vertically, then the Pythagorean theorem gives the tiny arclength ds:

Here, y' = dy/dx is the derivative/slope of y(x) with respect to x. The total length S of the curve, often referred to as the "action", is found by integrating these arclengths:

Notice that, unlike ordinary functions that take numbers as inputs, S[y] takes an entire curve y(x) as input and outputs a single number, S. Such a function is called a functional.

Our goal is to find the curve y(x) that minimizes the length S[y]. Instead of asking how the function (or "functional") S[y] changes for some change in x, we must ask how the total path length changes if we slightly deform the curve y(x) itself. This is the central idea behind a branch of calculus called "Calculus of Variations".

Let's predict that the shortest path between two curves is y(x). Without knowing that y(x) is a straight line (we haven't proved this yet!), our first prediction is probably incorrect. So, let's say the true ideal path is a nearby path y(x) + ε*η(x), where η(x) is the shape of the function that corrects our original prediction, and ε scales this correction function (i.e. controls how large the deformation is). To ensure that the endpoints remain the same, we require η(x₁) = η(x₂) = 0.

Substituting this new path into the expression for S[y] above, we get the path length S as a function of ε:

Notice that if ε = 0, we recover the original path y(x). If the original path does minimize S, then S has a minimum at ε = 0. That is,

So, to find the minimizing curve y(x), we find a curve y(x) such that dS/dε = 0 at ε = 0. Bringing the derivative inside of the integral for S[y] and using the chain rule, this means:

We can integrate this by parts using the substitutions:

Since we originally defined η(x₁) = η(x₂) = 0, the first term on the right disappears. We're left with:

Now, this must be true for any η(x) we choose, meaning:

Squaring both sides and rearranging to solve for y', we get that:

Hence, y(x) has a constant slope and is therefore a straight line. Note that there are multiple ways to prove that the shortest path between two points is a straight line (such as using the Triangle Inequality or algebraic arguments under Euclidean geometry), but this method of using least action has proven to have much deeper physical significance to more sophisticated questions, as discussed below.

As many of you have probably noticed, this argument only holds in flat Euclidean space (the space most of us are used to, like the xyz plane). In curved spaces, the analogue of a straight line is called a geodesic.

The general idea is still the same, but the formula for distance changes depending on the geometry of the space.

For example, consider the surface of the Earth. If you want the shortest route from Toronto to London while remaining on Earth's surface, you don't follow what looks like a straight line on a flat map. You follow part of a "great circle".

On a sphere of radius R, for example, the infinitesimal distance is given using spherical coordinates:

So instead of minimizing the flat-space length

we minimize:

Applying the same calculus-of-variations machinery gives the geodesics of the sphere, which turn out to be great circles.

This becomes especially interesting in general relativity. Spacetime itself is curved, and free-falling objects follow geodesics through that curved spacetime. In that sense, Earth's orbit around the Sun can be thought of not as Earth being forced away from a straight path, but as Earth following the natural geodesic of curved spacetime.

What we proved above is a remarkable fact, as the idea of minimizing the "action" functional is a fundamental characteristic of systems in nature. Instead of minimizing distance, physical systems obey the same mathematics to minimize quantities involving energy and time. For example, a hanging rope will form a shape called a catenary (a type of hyperbolic cosine function) to minimize its gravitational potential energy, not a parabola (which approximates the motion of projectiles) as one might think.

Similarly, light bends when moving between materials like air and water to minimize travel time.

So, never doubt the power of seemingly "simple" or intuitive mathematical results, as this elementary question of minimizing distance between two points uses the same mathematics that governs planetary motion, quantum fields, and spacetime itself!

The Birthday Paradox

callumneily — Fri, 31 Jul 2026 19:28:43 Z

Imagine walking into a room full of strangers. At first, everyone seems completely unrelated, with different names, different lives, and different birthdays. As the room gets more crowded, we of course know that sharing similarities with the strangers in the room becomes more likely.

But just how likely is it that, for example, two people in the room share a birthday?

The Birthday Paradox asks, more specifically: How many people must be in the room so that it is more likely than not that two people share a birthday?

Intuitively, we might imagine it must be well over 100 since there are 365 possible birthdays that each stranger can have. Probability, however, has a habit of ignoring our intuition.

Suppose there are only two people in the room: you, and one stranger. You have your own birthday, so there is a 1/365 ≈ 0.27% chance that the stranger next to you shares your birthday. If we add a third person to the room, we must consider the probability that either of the two strangers share your birthday, or the two strangers share a birthday with each other. This overlap makes our calculations more difficult, and it will only get worse as we add more people.

Instead, there's a cleaner way to find the solution. Let's find the probability P(n) that no two people share a birthday, then subtract it from 1 to get the probability that two people do share a birthday. Once P(n) < 0.5, then 1 - P(n) > 0.5, meaning at this value of n, there is a greater than 50% chance that two people in the room share a birthday.

For the case of only two people, there is a 364/365 chance that the second person has a different birthday than the first person. If we introduce a third person, there are 363 unclaimed birthdays left in the year, so a 363/365 chance that the third person's birthday falls on one of these days. Repeating this process for n people in the room, we get:

If we keep increasing n, we see that the probability P(n) that two people don't share a birthday decreases, meaning the probability 1 - P(n) that they do share a birthday increases.

Below are some values of 1 - P(n) I found using Maple Learn:

Based on these calculations, we see that when 23 people are in the room, it is more likely than not that at least two people in the room share a birthday.

Our question was very specific: we wanted to know what n needed to be in order for the probability of two people sharing a birthday to be > 50%. We found this number to be 23. But, we could extend this question to, say, the probability being 99.9%. This happens when there are 70 people in the room. Consider the animation below to see this visually, where the x-axis represents the number of people n, and the y-axis represents the probability that two people share a birthday, 1 - P(n):

How many people must be in the room to guarantee that two of them share a birthday? By the shape of the graph above, it may be tempting to think that it is impossible to guarantee this, since the probability seems to asymptotically approach 1. However, we must think through this logically.

If 365 people were in the room, it is still possible that their birthdays would all fall on a different day (although it's extremely unlikely). But if we have more people than we have available birthdays, we can say at least two people will share a birthday with 100% certainty. That is, P(n) = 0 when n > 365, so our formula for P(n) above is limited to values of n that are 365 or less. This is a basic principle in mathematics called the "pigeonhole principle", which uses the analogy that if we have more pigeons than we have holes to put them in, then at least one of the holes must have at least two pigeons.

These ideas can be conceptually confusing, but once you go through the mathematical arguments a few times, mentioning the Birthday Paradox in a conversation is a great way to show off your random math knowledge and confuse your peers!

Attached is an interactive Maple worksheet from Maple's Math Apps gallery if you'd like to investigate the Birthday Paradox further: The_Birthday_Paradox.mw. If you're still interested in more info/applications, see this Maple Primes post by John May.

Because $F17*(1+$B$4)^3 is not Engineering Documentation

Samir Khan — Thu, 23 Jul 2026 19:37:05 Z

We’ve just launched Maple Flow 2026. And yes, the title of this post doesn’t follow the standard formula for a “new version, new features” announcement.

But this is not only a post about a new version number and new features.

It is about a problem engineers know too well: important calculations trapped inside spreadsheets, scripts and old project files - visible only as cell references, code fragments and hidden logic.

That might be fine when the calculation is being built. It is not fine when the calculation must be reviewed, reused, defended, audited or handed to another engineer.

Maple Flow is built around a simple idea: engineering calculations should be readable, structured, auditable and easy to share.

The new release gives engineers better ways to create those calculations, bring existing work into Maple Flow, and turn legacy material into documentation that actually explains the engineering.

You can now generate Maple Flow worksheets using tools like Codex or Claude Code. This makes it easier to transform ideas into structured technical analyses and turn existing calculations into clear, auditable engineering documents.

Here are a few ways to use this new integration,

Rescue calculations from spreadsheets

Many engineering teams have years - sometimes decades - of calculations sitting in Excel. Those spreadsheets often contain real engineering value, but the logic is fragmented across cells, sheets and references.

Manually translating all that work into a clearer format can feel like too much effort to even start.

With Maple Flow 2026 and an AI coding assistant such as Codex or Claude Code, you can use a simple instruction like:

Convert this Excel spreadsheet to a Maple Flow worksheet

The AI coding assistant then helps transform spreadsheet logic into a structured Maple Flow worksheet with natural 2D math, clearer variable names and calculations that are visible on the page.

This is not just file conversion. It is a way to expose the engineering intent hidden inside legacy spreadsheets and make that work easier to understand, extend and review.

Generate engineering worksheets from a prompt

The same AI-assisted workflow can also help create a new Maple Flow worksheet from a natural language prompt.

Ask for a design calculation, describe the engineering context, specify the assumptions and ask for supporting text, equations, plots or diagrams. The result can be a worksheet that already contains readable math, explanations and visual context.

Stop hiding math in scripts

Maple Flow 2026 also opens up a practical path for moving useful work out of scripts and into worksheets that communicate the engineering more clearly.

For example, a Matlab script built around low-level loops, switch/case logic and numerical iteration can be translated into a Maple Flow worksheet that uses higher-level mathematical constructs.

In one example, a crude while-loop numerical solver became an fsolve call, and switch/case logic became a much more readable piecewise expression.

The point is not simply to change syntax. It is to move from code that performs a calculation to a worksheet that explains the calculation.

I've outlined a few ideas that demonstrate how you can use AI coding assistants to generate Maple Flow worksheets. There's much more you can do.

Of course, this functionality is subject to the same disclaimers as any other AI-generated content:

Worksheets are generated by and subject to the limitations of your AI model
your mileage may vary
and you absolutely need a (human) expert in the loop to check the output.

But as a starting point, this workflow is seriously powerful.

Set up your AI coding assistant to generate Maple Flow worksheets with the instructions here.

Maple Flow 2026 introduces in-worksheet programming, so you can write programs and scripts directly on the canvas and execute them inline with the rest of your worksheet.

You can enter programs with the keyboard or use the new Programming palette, which inserts common templates such as loops and if/else blocks with the right structure and indentation.

Productivity features help keep the program readable:

automatic closing of procedures, loops and conditionals after the opening line is entered
intelligent insertion of statement separators when appropriate
automatic indentation to preserve structure and readability

The existing Code Editor remains the right place for larger scripts. In-worksheet programming is for the logic you want to sit directly beside the math, text and results.

The Mathcad Migration Assistant has also been updated. It now translates much of the content of Mathcad Prime files into Maple Flow format, in addition to the existing support for Mathcad 13, 14 and 15.

Because Maple Flow 2026 now supports in-worksheet programming, the updated Migration Assistant also translates Mathcad programs.

For Mathcad thinking about moving to Maple Flow, the update makes migration faster and more practical. Existing work moves over with more of the structure and intent preserved.

Worksheets now compute faster! The results depend on the specific calculations, but the improvements can be very noticable - these are selected results from our internal benchmarking suite

For engineers working with larger or more complex worksheets, that kind of improvement changes the feel of the product.

Maple Flow 2026 adds more ready-to-use examples to the Application Gallery, including a new Semiconductor section.

The gallery now includes worksheets semiconductor oxide growth, heat sink sizing, cylindrical fin heat transfer, NMOS threshold voltage, MOSFET power loss, photolithography scanner limits and boron implantation stability.

Maple Flow 2026 also improves everyday interoperability with Excel. You can now paste more types of data from Excel into Maple Flow, including data formatted as percentages.

It is a small feature with a practical benefit: less cleanup, fewer interruptions and a smoother path from spreadsheet data to engineering calculation.

The most important thing about Maple Flow 2026 is not any single feature - it is the direction of travel. Engineering work is becoming more connected. Calculations are moving between spreadsheets, scripts, AI coding assistants, design tools and formal documentation.

Maple Flow gives those calculations a place where the math is visible, the structure is clear and the result is ready to share.

So yes, Maple Flow 2026 includes new features. But the bigger story is this: engineers can now move more of their existing work out of hidden logic and into auditable worksheets that communicate the engineering.

Explore the new features, try the examples and experiment with generating worksheets using AI coding assistants. Grab a trial here.