When working on the next version of ObjectiveFrame with improved scripting features, I got the idea to explore how well an AI agent would create a mechanical structure using the built-in scripting language (ChaiScript) in ObjectiveFrame. I had some initial sessions with Claude which confirmed that it was possible to create scripts from textual prompts and run them in the ObjectiveFrame's new scripting environment. This led me to my next idea of adding an interface to an AI agent directly in the ObjectiveFrame application. The AI agent would then be able to create mechanical structures based on the user's prompt. In the following sections, I will describe my process to implement this feature.
As I have mentioned in my previous post, I have been working on my graphics library Ivf++ 2.0. There are many features I would like to implement, but as this is a hobby project I have limited time to work on it. My common approach has been to select a feature to implement, do research online, and then start coding. You start with great energy and enthusiasm, but you often get stuck on small things as you progress. Why doesn't this approach work? How does this function work? Why is this not rendering correctly? These are some of the questions that come up. You can spend hours figuring out the problem; sometimes, you can't find the solution. This can be very frustrating and demotivating.
Some time ago, I started exploring AI tools. In 2022, I started experimenting with ChatGPT, first by letting it suggest improvements to existing codes in Python. I was amazed at the improvements it suggested. I also used it to answer questions on different programming topics. One thing that impressed me was the ability of the language model to translate numerical code written in Python and Numpy to C++ and Eigen. Worked almost perfectly. Later, during 2023, I used it in the refactoring of ObjectiveFrame. It helped me to refactor the codebase and make it more readable.
Another AI tool that I started using as an early adopter was Github Copilot from Microsoft. It is a code completion that uses a language model to provide the completions. For me, it has made it fun to code again. It's almost like having a pair of programmers who know everything and help out with the boring parts of coding. By combining classical chat-based AI with Copilot, I have implemented features in Ivf++ 2.0 at a pace that I would not have been able to do otherwise. It feels like coding at the speed of light. In the rest of this post, I will describe some examples of how I have used these tools in the development of Ivf++ 2.0.
However, my needs for 3d programming have not been for games but for interactive 3d applications mainly in the field of engineering. I have used OpenGL for many years. For my PhD I developed a 3D Scene Graph library, Ivf++, which was a wrapper around OpenGL. It contained a set of nodes for implementing interactive 3D applications, such as ObjectiveFrame a 3D beam analysis application focusing on real-time interaction.
During the last decade OpenGL has evolved and the fixed function pipeline has been deprecated. Modern OpenGL is based on shaders and the programmable pipeline. This has made OpenGL more powerful, but with the cost of complexity and ease of use. This article is about my journey implementing a new 3D graphics library for C++ that is easy to use and at the same time flexible. Ultimately I want to get back to ease of use of the fixed function pipeline but with the power of modern OpenGL.
CALFEM for Python is a comprehensive package designed for learning the finite element method. Originally developed in the 1980s using Fortran, CALFEM has since evolved to support MATLAB and Python. The Python version was specifically created to facilitate the course "Software Development for Technical Applications" at Structural Mechanics in Lund. Throughout the course, students gain hands-on experience in developing a complete finite element application in Python. They learn how to implement various components, including mesh generation, solver, user interface, and visualization routines.
In this blog post, I will provide a detailed explanation of the packaging and installation process for CALFEM.
When redesigning an application, there is an opportunity to rethink the existing design and make improvements. In the case of ForcePAD 2, it was a simple pixel-based drawing application with a single layer for drawing. Users could add loads and boundary conditions on top of the drawing, and results were displayed on top of the image layer.
With ForcePAD 3, I aim to introduce a layered model that allows users to add multiple drawing layers on top of each other, similar to how a designer or architect uses sketch paper. These layers can be hidden and made transparent, providing more flexibility when sketching. In the following sections, I will provide a detailed description of the model and the main classes that implement it.
Choosing the right libraries for your application is just one step in making it run on multiple operating systems. ForcePAD uses CMake to build the application on multiple platforms. CMake is an excellent tool that generates build files for each platform. On Windows it generates Visual Studio solution files. On other platform it generates make files. I started implementing most of the tooling for ForcePAD on Windows thinking that most of this can be easily transferred to macOS. This what not that easy. I will go through the setup in the following sections.
The current version of ForcePAD is implemented using the FLTK library and OpenGL for graphics. At the time FLTK provided an easy way of implementing an multiplatform application that could run on Windows and Unix/Linux. FLTK provided both an intuitive environment for designing the 2D user interface as well as easy OpenGL context creation. OpenGL context creation on Windows and Unix was implemented very differently on each platform. Having just one way of this enabled ForcePAD to be implemented in a single code base.
ForcePAD was developed as part of my PhD thesis almost 20 years ago. The application was developed in C++ using OpenGL for graphics and FLTK for the user interface. At the time, it was state-of-the-art and compiled on all platforms, such as Linux, Windows, and SGI Irix 6.5(!). The application was used extensively in teaching and is still used today.
