At Maplesoft, we are excited to be celebrating our 30^th year of incorporation. This anniversary is a tremendous milestone for us. As a leading provider of mathematics-based software solutions for science, technology, engineering and mathematics (STEM), this longevity attests to our ability to grow along with changing market conditions, to continually enhance the quality of our offerings and strengthen our partnerships with industry leaders.

As a company, it is our goal to actively connect and partner with our users and industry leaders to advance STEM education and continue to revolutionize engineering design processes. When it comes to academics, we believe our partnerships and outreach initiatives help improve STEM education, develop and enhance digital learning tools and foster online education. To that end, Maplesoft is an Affiliate Member of the Fields Institute, Educational Outreach Champion of Perimeter Institute, and Technology Partner of the American Math Society’s “Who Wants to be a Mathematician” student competition. On the commercial side, we work closely with our commercial partners to seamlessly integrate our technology with complementary tools. Our relationships with prominent companies such as Rockwell Automation, B&R, Altair and more, allow us to continue leading this charge.

At Maplesoft, we work continuously to improve our technology offerings by developing new products and enhancing our existing technology. Maple 2018, the newest version of our flagship product Maple, offers new and improved features to benefit all users, no matter what they use Maple for. It provides an environment where students and instructors can enrich the classroom experience, researchers can accelerate their projects and engineers can refine their calculation management processes. Möbius, our online courseware platform, enables instructors to author rich content, explore important STEM concepts using engaging, interactive applications, visualize problems and solutions, and test students’ understanding by answering questions that are graded instantly.

On the engineering side, we are revolutionizing the engineering design process using Digital Twins, which are virtual machine designs created in MapleSim, Maplesoft’s modeling and simulation software. By taking a virtual approach to machine-level system integration, engineers can commission faster, earlier, and with less risk.

Maplesoft has come a long way since our humble beginnings as a research project at the University of Waterloo. Our website features a timeline that provides insights and information on our incredible journey. The company was built on a foundation of creativity and passion for mathematics and we have worked hard to preserve that legacy. The growth experienced over the past 30 years, along with the drive of our global employee and partner base, will ensure Maplesoft continues to be a driving force in the world of online education and design engineering long into the future.

We invite you to join us as we continue our journey towards new and exciting developments and innovations.

Due to the mechanistic process of our students and little creativity in analysis in schools and universities to be professionally trained is that STEM education appears (science, technology, engineering and mathematics) is a new model that is being considered in other countries and with very slow step in our city. In this work the methods with STEM will be visualized but using computational tools provided by Maplesoft which is a company that leads online education for adolescents and adults in the current market. In Spanish.

ECI_UNT_2018.pdf

ECI_UNT_2018.mw

Lenin Araujo Castillo

Ambassador of Maple

Download hilbert.mw

To demonstrate Maple 2018’s new Python connectivity, we wanted to integrate a large Python library. The result is the DeepLearning package - this offers an interface to a subset of the Tensorflow framework for machine learning.

I thought I’d share an application that demonstrates how the DeepLearning package can be used to recognize the numbers in images of handwritten digits.

The application employs a very small subset of the MNIST database of handwritten digits. Here’s a sample image for the digit 0.

This image can be represented as a matrix of pixel intensities.        

The application generates weights for each digit by training a two-layer neural network using multinomial logistic regression. When visualized, the weights for each digit might look like this.

Let’s say that we’re comparing an image of a handwritten digit to the weights for the digit 0. If a pixel with a high intensity lands in

• an intensely red area, the evidence is high that the number in the image is 0
• an intensely blue area, the evidence is low that the number in the image is 0

While this explanation is technically simplistic, the application offers more detail.

Get the application here

Using Maple's native syntax, we can calculate the components of acceleration. That is, the tangent and normal scalar component with its respective units of measure. Now the difficult calculations were in the past because with Maple we solved it and we concentrated on the interpretation of the results for engineering. In spanish.

Calculo_Componentes_Aceleracion_Curvilínea.mw

Uso_de_comandos_y_operadores_para_calculos_de_componentes_de_la_aceleración.mw

Lenin Araujo Castillo

Ambassador of Maple

The Maple splash screen needs a makeover, it's not too exciting so looking at the maplesoft website the opening screen has an image that would have been rather fitting for the Maple 2018 splash screen.  Here's the image I'm talking about.