Python C++ PDE
modmesh seamlessly mixes C++ and Python through pybind11, allowing you to leverage the strengths of both programming languages for efficient PDE solving. We use Qt and Python to visualize the computation results to give you a better understanding of your PDE solution. modmesh also supports mesh visualization, currently in the Gmsh mesh file format. We have recently made efforts to improve the modmesh UI/UX.
The design allows it to run on Windows, Linux, and MacOS. Everyone can use or contribute to modmesh.
Python Translation Translator Toolkit
We are a passionate and dedicated volunteer group committed to bridging the language gap in the Python community. Our primary mission is to make Python's official documentation accessible to the Traditional Chinese audience by providing accurate and comprehensive translations. Whether you are a seasoned Python developer or a language expert, we welcome individuals who are enthusiastic about Python and passionate about making knowledge accessible to all. Come join us in our monthly meetups where we share insights, discuss translation challenges, and build a collaborative community together!
Sciwork Portal is a project for maintaining our official website - Sciwork.dev, which was built by Pelican with tailwindCSS, and deployed by Netfliy. We create the promotional pages for meetup and sprint events. Our team also maintains the sciwork conference page - conf.sciwork.dev.
We have always been actively trying to provide users a better web browsing experience, including information presentation and visual experience. Welcome to join us if you are interested in website maintence.
Common errors such as the misuse of synonyms and homophones are prevalent for both native and non-native Taiwan Mandarin users. This issue is indirectly attributed to the fact that large language models (LLMs) trained on data primarily sourced from the internet struggle to play a reliable role in Mandarin grammar checking tasks. Moreover, the token-by-token and linear training process of the LLMs cannot provide valid knowledge based on a thorough understanding of the non-linear structure of human language. However, from a linguistic perspective, training a model solely based on a mass amount of data is not the most direct solution. In reality, such errors are directly related to the non-linear structures of word classes and phrase structures. By analyzing and simplifying the structures of correct usages into code instructions, a linguistics-based model can effectively grasp the use of language in a manner similar to human native speakers. This enables the model to accomplish efficient Mandarin grammar checking tasks with minimal resources.
uTensor is an extremely lightweight machine learning inference framework built on C++11. It simplifies model deployment by seamlessly converting TensorFlow-trained models into efficient C++ files that can be used to infer on the embedding device and integrate with optimized libraries such as CMSIS-NN by ARM with ease. Compared with the binary files, C++ source code will provide greater flexibility to modify the trained model for the embedding engineers.
We provide the defaults for tensors, operators, and memory allocation. Just like the booming development of machine learning, we are also actively developing the above functions. Welcome to join us.
Cytnx (pronounced as sci-tens) is a tensor network library designed for classical/quantum physics simulations. It supports C++ and Python with almost identical interface and syntax, such that users can effortlessly switch between the two languages. Aiming at a quick learning process for new users of tensor network algorithms, the interfaces resemble the popular scientific libraries such as numpy, Scipy, and PyTorch. Symmetries present in physical systems can be easily defined and implemented in tensors. In addition, we provide a useful class called Network that allows users to store large tensor networks and perform the contractions in an optimal order that can be automatically computed.
There are still many physics applications and GPU support backend being developed. Welcome to join us to contribute to Cytnx.
Commitizen-tools allows teams to effectively implement version control that conforms to the specified rules. It uses interactive forms to create commit messages that meet conventional commits rules. It also bumps versions automatically using Semantic Versioning and generates a changelog using Keep a Changelog.
In addition, you can also customize your own rules to suit your team. Commitizen-tools is a widely used tool in many communities. We still have a lot of issues to fix and develop. Welcome to join us.
You can join the Discord group to discuss the project.