Open-source alternatives to CFDPython

This project is a parallel simulation engine and molecular dynamics simulator designed to model the physical movements of atoms and molecules. It functions as an interatomic potential framework for calculating forces between particles and a materials analysis tool for computing thermodynamic, structural, and transport properties of solids and fluids. The engine is distinguished by its high-performance computing capabilities, utilizing spatial-domain decomposition and message-passing interface communication to distribute workloads across processors. It supports multi-backend GPU acceleration v

This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi

jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

This is a TensorFlow learning course and machine learning education resource. It is a notebook-based interactive course that provides a deep learning tutorial series and a guide to the Keras API through executable Python code and formatted text. The material focuses on deep learning education, covering the implementation of TensorFlow models and the design of neural network architectures such as multilayer perceptrons and convolutional networks. It includes instructional content on constructing custom training loops and dataset generators for data pipeline engineering. The course covers mach

This project is a framework and curriculum for self-directed learning, providing a structured methodology for mastering complex technical skills without formal instruction. It combines educational content with a technical study methodology centered on deliberate practice and the psychological habits required for independent mastery. The project is distinguished by its use of interactive notebooks and markdown documentation to deliver a sequenced learning path. It integrates test-driven development patterns into the educational process to provide automated feedback and resolve cognitive barrie

Mixbox is a specialized simulation engine for modeling fluid color dynamics, pigment-based hue shifts, and latent space color blending. It functions as an RGB color mixing simulator that uses pigment-based simulations and latent space interpolation to produce realistic results. The project incorporates a Kubelka Munk color engine to achieve physically accurate hue shifts and saturated gradients. It also utilizes a latent space color blender to transform colors into latent representations for precise linear interpolation and multi-color mixing. The system covers fluid dynamics for calculating

QuantumKatas is a set of quantum computing courseware and educational assets designed to teach the Q# programming language and quantum computing principles. It combines structured tutorials and coding tasks with interactive notebooks and a dedicated unit testing suite to validate the correctness of exercise implementations. The project provides a dockerized learning environment that packages all necessary tools and dependencies into a virtual image. This allows for the execution of quantum programming exercises without the need for local software installation. The curriculum covers qubit man

This project is a machine learning coursework repository containing a collection of Python exercises and notebooks. It is designed for implementing foundational machine learning algorithms and completing curriculum assignments through interactive documents that combine instructional text and executable code. The repository provides code formatted for compatibility with automated grading systems, allowing for the submission and validation of technical exercises. It includes predefined environment configurations and dependency locks to ensure consistent execution of data science tools across di

This project is a collection of interactive Jupyter notebooks and a structured machine learning tutorial series. It serves as an educational resource for studying predictive modeling and statistical analysis through a curriculum of executable code examples. The notebooks are specifically designed to accompany video tutorials, integrating external video assets with live code to synchronize visual instruction with hands-on experimentation. This approach allows users to follow sequential lessons while executing and modifying machine learning workflows directly in a browser. The content covers t

This project is a GPU-accelerated fluid renderer and interactive WebGL visualizer. It functions as a fluid dynamics sandbox for experimenting with the real-time movement and behavior of liquids. The simulation calculates density, pressure, and vorticity using GPU shaders to maintain high performance. It includes atmospheric visual effects such as bloom and sunrays, alongside tools for capturing the simulation state as static images. The underlying architecture utilizes fragment-shader rendering and ping-pong texture buffering to manage fluid physics. Multi-pass post-processing is used to app

This project is a comprehensive collection of Python programming education materials, including tutorials, exercises, and curated code samples. It serves as a learning curriculum and software engineering toolkit, utilizing Jupyter Notebooks to combine executable code with descriptive educational text. The repository provides practical implementation guides for building large language model applications, such as retrieval-augmented generation systems, stateful AI agents, and machine learning workflows. It distinguishes itself by offering a structured approach to agentic coding workflows, cover

This project is a collection of linear algebra educational notebooks and study resources. It serves as a mathematics study resource providing structured notes and explanations for learning core algebraic concepts. The material is authored as interactive math tutorials and LaTeX mathematical courseware, combining theoretical explanations with executable mathematical examples. Content is delivered through markdown-based study resources and converted into static site mathematics notes for serverless web access. The curriculum covers a range of mathematical theory, including matrix elimination,

ML-foundations is a machine learning educational curriculum and computer science study guide. It provides a structured learning path focused on the mathematical foundations and computational prerequisites required for studying machine learning. The project serves as a Python mathematics course, delivering interactive notebooks and coding exercises to teach linear algebra, calculus, and statistics. It translates abstract mathematical formulas into concrete algorithmic code to help learners understand the principles underpinning machine learning algorithms. The curriculum covers data science p

This is an educational curriculum for building and training neural networks using PyTorch. It serves as a deep learning training guide and resource, providing a structured series of lessons on tensor computation and architecture development. The course uses an interactive learning model that synchronizes academic theory with practice. It pairs theoretical lecture slides with exercise-driven notebooks, requiring students to implement model logic within predefined templates to validate their conceptual understanding. The curriculum covers a broad range of deep learning capabilities, including

An interactive Python code notebook and machine learning tutorial repository, this project provides a collection of instructional guides and code examples explaining data science concepts and predictive modeling techniques. It specifically functions as a scikit-learn tutorial notebook containing educational documents that demonstrate machine learning algorithms and practical workflows. The repository supports data science tutorial authoring, machine learning education, and interactive notebook learning. It organises content into sequential, self-contained computational steps that combine narr

This project is a comprehensive educational curriculum for learning data science and predictive modeling using the Python programming language. It provides structured instructional material and guides covering supervised learning, unsupervised learning, and neural network design. The curriculum focuses on building, training, and evaluating machine learning models. It includes specific guides for implementing linear regression, decision trees, and support vector machines for predictive analysis, as well as tutorials on designing convolutional and recurrent neural network architectures. The co

The Powder Toy is a C++ physics engine and material simulation software that operates as a falling sand physics sandbox. It provides a simulation environment where solids, liquids, and gases interact based on heat, pressure, and velocity. The project is a scriptable sandbox environment, offering an external scripting interface and behavioral scripting to modify game mechanics, automate tasks, and define custom simulation logic. The software supports the construction of complex machinery, physical structures, and functional logic circuits through simulated electronic components and sensors. I

LiquidFun is a 2D physics engine and game physics framework designed to calculate movement and collisions for rigid bodies and particle systems. It functions as a simulation tool for integrating real-time physical interactions and dynamics into interactive applications. The framework specifically provides a particle-based fluid simulator to model liquid dynamics, including splashing, displacement, and surface tension. It also includes a soft body physics simulator for creating deformable and elastic objects that react to physical forces. The engine covers a broad range of physical interactio

FluidX3D is a GPU-accelerated computational fluid dynamics solver and voxel-based fluid simulator. It utilizes the lattice Boltzmann method to simulate gas and liquid behavior, pressure, and velocity through an OpenCL-based hardware abstraction that supports both graphics processors and CPUs. The system specializes in multi-phase flow simulation using volume-of-fluid methods for free surface modeling and droplets. It includes a thermal simulator for modeling heat transfer and thermal convection, alongside tools for aerodynamic force analysis to calculate lift, drag, and torque on physical obj

PhysX is a physics engine SDK designed for calculating real-time rigid body dynamics, fluid simulations, and environmental interactions in virtual applications. It includes a GPU-accelerated physics solver for computing complex particle fluids and combustion models, a voxel fluid simulator for real-time gas, fire, and smoke, and a destruction simulation framework for modeling the fracture of meshes. The SDK features a specialized machine learning physics tensor interface that enables the exchange of simulation data with machine learning frameworks using a common tensor format. It also impleme

DeepXDE is a scientific machine learning library and deep learning PDE solver used to compute solutions for forward and inverse ordinary, partial, and integro-differential equations. It functions as a physics-informed neural network library that embeds physical laws and boundary conditions directly into the neural network loss function. The project provides a deep operator network framework for learning operator mappings that approximate relationships between functions in multiphysics problems. It is implemented as a multi-backend tensor library, allowing the system to switch between differen

mplfinance is a financial time-series plotter and market data visualization framework built on Matplotlib. It is designed to render market data frames into specialized charts, including candlesticks, OHLC bars, Renko bricks, and point-and-figure columns. The library distinguishes itself through a dedicated market data framework that manages trading calendars and non-trading periods, ensuring accurate temporal spacing by collapsing gaps during holidays. It also provides a system for technical analysis charting, enabling the overlay of moving averages, volume bars, and other technical indicator

Gonum is a numerical computing library for the Go programming language, providing a collection of packages for scientific computing, linear algebra, statistics, and optimization. It functions as a framework for performing complex numerical computations and solving systems of linear equations. The project includes a dedicated graph analysis framework for modeling network graphs and solving connectivity and pathfinding problems. It also provides a statistical analysis toolkit for computing descriptive and inferential statistics and estimating mixture entropy. The library's capability surface c

This project is a structured curriculum archive and study resource for mastering deep learning architectures and model implementation. It serves as a categorized repository of academic materials, including courseware and implementation guides for neural networks. The collection provides a multi-model framework for building and training various architectures, specifically covering basic neural networks, convolutional networks, and sequence models. It focuses on deep learning architecture, regularization, and the process of structuring machine learning projects and tuning hyperparameters. The

This repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization. The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of ad

This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed

Plotly.py is a comprehensive framework for building production-ready data applications and interactive dashboards directly from Python code. It functions as both a high-performance visualization library for browser-based charts and a full-stack tool for transforming analytical scripts into responsive, web-based interfaces. By abstracting away the need for manual HTML or JavaScript, it allows developers to define complex layouts and functional logic using modular, reusable components. The framework distinguishes itself through a robust architecture that handles event orchestration and state sy