Dev Conference by Neo4j
Session Track: Data Science
Session Time:
Session description
Calculating physical product design is a complicated problem. Existing models provide rough examples in reasonable times but still require weeks of manual correcting. This presentation explores how graphs can help mechanical engineering by modeling geometric polygonal figures into a graph representation. Neural networks can then train and learn from examples provided to compute the midcurve of a thin polygon, becoming a graph-to-graph operation akin to an encoder-decoder problem.
AI Advisor
More than 20 years in Computer-aided-Design/Engineering research, software development and management. Got Bachelors, Masters and Doctoral degrees in Mechanical Engineering (specialization: Geometric Modeling Algorithms). Currently helping people/organizations in their AI journeys, in fields such as Data Science, Artificial Intelligence Machine-Deep Learning (ML/DL) and Natural Language Processing (NLP).
