Part A: Innovative Data Augmentation Approach to Enhance Machine Learning Efficiency—Case Study for Hydrodynamic Purposes

Part A: Innovative Data Augmentation Approach to Enhance Machine Learning Efficiency—Case Study for Hydrodynamic Purposes

These days, AI and machine learning (ML) have become pervasive in numerous fields. However, the maritime industry has faced challenges due to the dynamic and unstructured nature of environmental inputs. Hydrodynamic models, vital for predicting ship responses and estimating sea states, rely on diver...

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Bibliographic Details
Main Authors: Hamed Majidiyan, Hossein Enshaei, Damon Howe, Eric Gubesch
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Applied Sciences
Subjects:
machine learning
AI
seakeeping
data analysis
numerical modelling
feature engineering
Online Access:https://www.mdpi.com/2076-3417/15/1/158
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