Python for Ship Design: Data Processing and Machine Learning with the ShipD Dataset
EVENT DESCRIPTION
At SNAME CAS, we recognize the essential role of programming as a critical skill for engineers and designers, enabling them to perform complex calculations and simulations in ship design.
We are excited to announce that our upcoming session will feature the Navalapp team as guest speakers. They will provide an exclusive presentation on Python for Ship Design, offering valuable insights and practical knowledge tailored to both students and industry professionals.
Python, an open-source programming language, allows anyone to download, use, and modify it at no cost. Additionally, users can contribute to its development, as it is supported by a global community of developers under the Python Software Foundation.
Navalapp is a renowned provider of specialized courses for the Yacht Design, Naval Architecture, and Ocean Engineering industries.
Note: The event is virtual ONLY
Navalapp will provide an overview of Python programming in this webinar, followed by a live demonstration of data processing and machine learning using Python and the ShipD dataset from MIT, which includes over 30,000 different hull geometries.
The one-hour session structure is as follows:
• Pablo – Brief introduction to Navalapp.
• Javier – Overview of Python: theory, code structure, and real-world application examples of Data Processing and Machine Learning.
• Antonio – Live demonstration using Google Colab and the MIT dataset, showcasing data processing and machine learning in action.
• Pablo – Brief introduction to the Introduction to Python course.
SPEAKERS/PRESENTERS
Pablo Sopelana
• Entrepreneur and Industrial Engineer with extensive experience in the automotive, naval, and IT industries. Passionate about the ocean, naval architecture, yacht design, and sailing craft.
• Navalapp Founder / CEO.
Javier Capel
• Aeronautical Engineer and Ph.D. candidate specialized in computational methods, particularly computational fluid dynamics (CFD). His research focuses on leveraging deep learning to develop advanced turbulence models, enhancing the understanding of complex flow physics.
• Navalapp Instructor.
Antonio Medina
• Naval Architect and Ph.D. candidate, with extensive experience in experimental research on floating systems that involve extensive data analysis. His work focuses on applying recurrent neural networks (RNNs) to predict the response of offshore wind turbines.
• Navalapp Instructor.
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