List of our ongoing projects. As a software think tank, we are also available for individual projects with customers.
Computer Aided Engineering (CAE) is a key component modern engineering. Many commercial tools exist in this working domain, but they are usually expensive and lack highly in usability compared to modern software. Also data access is quite difficult, requiring to run scripts within commercial environments, which is slow and prohibits the development of new, shareable Libraries and Apps.
"We don't want to provide another software solution,
we want, that people can build their own solutions quickly"
The qd cae library shall give access to simulation data itself in a modern way and provide tools to handle CAE data in a comfortable manner.
"We believe, that CAE can not evolve if people do not get comfortable access to the raw data and start creating their own solutions, because creativity originates from freedom. This core idea keeps us pushing forward."
Even though the package/project is written for python, the libraries core is written in C++ for a high performance.
awesome-cae is a curated list of awesome CAE frameworks, libraries and software. We try to keep this repo up to date and add all the new resources we can find. This repo is a very helpful adress if one is searching for existing solutions.
qd-ansa-extension is a python library for ANSA/META from Beta CAE Systems, which shall make scripting much more comfortable and also provides some further features. The original API simply didn't seem pythonic enough to us and didn't yield the desired productivity.
Applying Machine Learning (ML) on CAE data is still a very young field. This has two major reasons:
- CAE data is difficult to access
- CAE data is very inhomogenious
The library qd-cae-python is our basis for ML, since it provides us better data access. Postprocessors are simply too slow and usually have a bad API, because they are not meant for advanced data analysis. Based on this, we develop algorithms and test new ideas, in order to find out, how to use ML for CAE in a meaningful and efficient way. This research is still disclosed, thus for further interest please contact us personally.