Dear Colleagues,

The advancement of deep learning and its increased implementation in various fields of engineering and science, partially due to the accessibility of high computational power and big data management systems, has also broadened the range of possibilities for offshore energy system structural analysis, performance modeling, active and passive control design, and power output optimization. The challenges of modeling these systems under real-world operating conditions often hinder the development process, particularly in the case of wave energy converters and floating offshore wind turbines. These challenges are primarily due to the nonlinear behavior of the system and the complications involved in simulating the fluid–structure interaction, principally for arrays of these devices. Although they are used for modeling purposes, high-fidelity numerical simulations have unique challenges, such as the incorporation of noisy data into the modeling procedure, the complexity of mesh generation for systems with complicated geometries, and the high dimensionality of parameterized partial differential equations. Further, the capabilities of classical numerical approaches for solving inverse problems and for system identification purposes are limited.

The aim of this Special Issue is to compile data-driven and physics-informed machine learning approaches to study forward and inverse problems involved in offshore energy systems. This includes, but is not limited to, physics-informed dynamic modeling of offshore wind turbines and wave energy converters, power generation modeling and power optimization, data-driven modeling and optimization of offshore energy system arrays, implementation of neural networks and machine learning techniques for control system design, and data-driven approaches for fluid–structure interaction simulations for offshore energy systems. This Special Issue welcomes studies covering various offshore energy devices, from small-scale systems used for powering sensors and monitoring devices to an array of utility-scale energy conversion systems.

Dr. Masoud Masoumi
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• marine energy
• offshore wind
• offshore energy systems
• data-driven modeling
• physics-informed neural networks
• machine learning