What is mechanical-cfd-ai-research-hub?

This repository is a curated research and learning hub for mechanical engineering, computational fluid dynamics, and scientific artificial intelligence. Rather than containing its own simulation code, it acts as a navigation and explanation guide that organizes independent open source resources into structured learning pathways. It links out to upstream projects instead of copying their code, and each external project keeps its own separate license. The hub covers several connected topics: computational fluid dynamics and numerical methods, mechanical and aerospace engineering applications, machine learning applied to fluid mechanics, dynamic mode decomposition and Koopman based reduced order modeling, physics informed and scientific machine learning, finite element and multiphase simulation workflows, image analysis, and scientific writing and presentation skills. Visitors are offered three broad pathways to choose from depending on where they are starting: building foundations in mathematics, Python, and numerical methods, developing engineering models through CFD, finite element analysis, meshing, and verification and validation, or applying scientific AI methods such as physics informed neural networks, neural operators, and surrogate models. A roadmap diagram in the README shows how these pathways connect, moving from foundational math and programming through numerical engineering and machine learning basics, into more advanced topics like reduced order models and full research systems, ending with communication skills for writing papers and giving presentations. The repository also highlights specific featured research pathways that combine several of these topics into a single applied workflow, such as going from medical scan data through segmentation and simulation to build patient specific digital twins, or optimizing turbomachinery designs by combining simulation, experiments, and surrogate models. As of this snapshot the hub catalogs 57 curated resources in total, with dedicated pages for learning paths, project guides, and a full resource catalog with a selection guide to help readers pick the right material for their goals.