Machine Learning for Naval Architecture, Ocean and Marine Engineering
- URL: http://arxiv.org/abs/2109.05574v1
- Date: Wed, 1 Sep 2021 09:36:12 GMT
- Title: Machine Learning for Naval Architecture, Ocean and Marine Engineering
- Authors: J P Panda
- Abstract summary: This article reviews the applications of machine learning algorithms in naval architecture, ocean, and marine engineering problems.
We discuss the applications of machine learning algorithms for different problems such as wave height prediction, calculation of wind loads on ships, damage detection of offshore platforms, calculation of ship added resistance.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Machine Learning (ML) based algorithms have found significant impact in many
fields of engineering and sciences, where datasets are available from
experiments and high fidelity numerical simulations. Those datasets are
generally utilized in a machine learning model to extract information about the
underlying physics and derive functional relationships mapping input variables
to target quantities of interest. Commonplace machine learning algorithms
utilized in Scientific Machine Learning (SciML) include neural networks,
regression trees, random forests, support vector machines, etc. The focus of
this article is to review the applications of ML in naval architecture, ocean,
and marine engineering problems; and identify priority directions of research.
We discuss the applications of machine learning algorithms for different
problems such as wave height prediction, calculation of wind loads on ships,
damage detection of offshore platforms, calculation of ship added resistance,
and various other applications in coastal and marine environments. The details
of the data sets including the source of data-sets utilized in the ML model
development are included. The features used as the inputs to the ML models are
presented in detail and finally, the methods employed in optimization of the ML
models were also discussed. Based on this comprehensive analysis we point out
future directions of research that may be fruitful for the application of ML to
the ocean and marine engineering problems.
Related papers
- Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey [51.87875066383221]
This paper introduces fundamental concepts, traditional methods, and benchmark datasets, then examine the various roles Machine Learning plays in improving CFD.
We highlight real-world applications of ML for CFD in critical scientific and engineering disciplines, including aerodynamics, combustion, atmosphere & ocean science, biology fluid, plasma, symbolic regression, and reduced order modeling.
We draw the conclusion that ML is poised to significantly transform CFD research by enhancing simulation accuracy, reducing computational time, and enabling more complex analyses of fluid dynamics.
arXiv Detail & Related papers (2024-08-22T07:33:11Z) - Physics Informed Machine Learning (PIML) methods for estimating the remaining useful lifetime (RUL) of aircraft engines [0.0]
This paper is aimed at using the newly developing field of physics informed machine learning (PIML) to develop models for predicting the remaining useful lifetime (RUL) aircraft engines.
We consider the well-known benchmark NASA Commercial Modular Aero-Propulsion System Simulation System (C-MAPSS) data as the main data for this paper.
C-MAPSS is a well-studied dataset with much existing work in the literature that address RUL prediction with classical and deep learning methods.
arXiv Detail & Related papers (2024-06-21T19:55:34Z) - Replication Study: Enhancing Hydrological Modeling with Physics-Guided
Machine Learning [0.0]
Current hydrological modeling methods combine data-driven Machine Learning algorithms and traditional physics-based models.
Despite the accuracy of ML in outcome prediction, the integration of scientific knowledge is crucial for reliable predictions.
This study introduces a Physics Informed Machine Learning model, which merges the process understanding of conceptual hydrological models with the predictive efficiency of ML algorithms.
arXiv Detail & Related papers (2024-02-21T16:26:59Z) - Advancing Reacting Flow Simulations with Data-Driven Models [50.9598607067535]
Key to effective use of machine learning tools in multi-physics problems is to couple them to physical and computer models.
The present chapter reviews some of the open opportunities for the application of data-driven reduced-order modeling of combustion systems.
arXiv Detail & Related papers (2022-09-05T16:48:34Z) - SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines.
This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
arXiv Detail & Related papers (2021-12-22T14:45:37Z) - A Review of Physics-based Machine Learning in Civil Engineering [0.0]
Machine learning (ML) is a significant tool that can be applied across many disciplines.
ML for civil engineering applications that are simulated in the lab often fail in real-world tests.
This paper reviews the history of physics-based ML and its application in civil engineering.
arXiv Detail & Related papers (2021-10-09T15:50:21Z) - Bridging observation, theory and numerical simulation of the ocean using
Machine Learning [0.08155575318208629]
The study of the ocean poses a combination of unique challenges that ML can help address.
The observational data available is largely spatially sparse, limited to the surface, and with few time series spanning more than a handful of decades.
This review covers the current scientific insight offered by applying ML and points to where there is imminent potential.
arXiv Detail & Related papers (2021-04-26T12:11:51Z) - Model-Based Deep Learning [155.063817656602]
Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques.
Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance.
We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
arXiv Detail & Related papers (2020-12-15T16:29:49Z) - A Survey on Large-scale Machine Learning [67.6997613600942]
Machine learning can provide deep insights into data, allowing machines to make high-quality predictions.
Most sophisticated machine learning approaches suffer from huge time costs when operating on large-scale data.
Large-scale Machine Learning aims to learn patterns from big data with comparable performance efficiently.
arXiv Detail & Related papers (2020-08-10T06:07:52Z) - Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and
On-Device Inference [49.88536971774444]
Inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots.
Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services.
We present and release the Oxford Inertial Odometry dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research.
arXiv Detail & Related papers (2020-01-13T04:41:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.