Artificial Intelligence in Mechanical Engineering

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dc.contributor.authorARVIND PATTAMATTA
dc.date.accessioned2026-11-01T07:15:57Z
dc.date.issued2026-10-31
dc.descriptionhe first is the AI that mechanical engineers already know. Machine learning finds patterns in data. Neural networks can estimate drag, identify a damaged bearing, classify defects in an infrared image or act as a fast stand-in for a costly simulation. Optimization algorithms search through shapes, materials and operating conditions. Physics-informed neural networks (PINNs) and neural operators try to bring data and governing equations together. These are numerical tools, and they are normally built for a particular task. The second is newer: large language models (LLMs) and coding agents. They work through ordinary language, equations, software and documents. An engineer can ask for a first derivation, a MATLAB script, an OpenFOAM case, a Python image-processing pipeline, a parameter study or an explanation of why a calculation failed. A coding agent can inspect several files, edit them, run tests and read the resulting errors before trying again. The important change is not that the machine “knows engineering” in the human sense. It is that much of the translation between an idea and a working computational experiment can now be assisted.
dc.description.abstractWhen people hear “artificial intelligence,” they usually picture a computer scientist training a chatbot or writing software. A mechanical engineer designing a pump, studying a crack, measuring a flame or building a heat exchanger does not fit that picture. That view is already out of date. Mechanical engineering has always joined mathematics to the physical world. We write equations for motion, heat, fluid flow and stress; turn them into numerical methods; build experiments; and decide whether the answer makes physical sense. AI is beginning to help at every one of those steps. It is not replacing mechanics with computer science. It is becoming another way of doing mechanics
dc.description.sponsorshipAUSTRALIAN BUSINESS DEANS COUNCIL
dc.identifier.urihttps://demo.dspace.org/handle/10673/1184
dc.language.isoen_US
dc.publisherABDC
dc.subjectARTIFICIAL INTELLIGENCE
dc.subjectTECHNOLOGY
dc.subjectDATA INTEGRATION
dc.subjectGENERATIVE AI
dc.titleArtificial Intelligence in Mechanical Engineering
dc.title.alternativeARTIFICIAL INTELLIGENCE
dc.typeArticle

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