A Physics-Informed Neural Network for the Prediction of Unmanned Surface Vehicle Dynamics

Xu, Pengfei; Han, Chen-Bo; Cheng, Hong; Chen, Cheng; Ge, Tida

doi:10.3390/jmse10020148

Cited by 18 publications

(6 citation statements)

References 13 publications

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“…The effectiveness of the algorithm is validated through simulation testing. Xu et al applied a physics-informed neural network for parameter identification of a three-degree-of-freedom motion model in unmanned ships [101]. By combining data-driven and physical model advantages, they constructed a loss function for predicting the motion attitude of unmanned ships based on velocity and steering models.…”

Section: Ship Extreme Short-term Motion Predictionmentioning

confidence: 99%

A Review on Motion Prediction for Intelligent Ship Navigation

Zhang,

Chu,

Liu

et al. 2024

JMSE

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In recent years, as intelligent ship-navigation technology has advanced, the challenge of accurately modeling and predicting the dynamic environment and motion status of ships has emerged as a prominent area of research. In response to the diverse time scales required for the prediction of ship motion, various methods for modeling ship navigation environments, ship motion, and ship traffic flow have been explored and analyzed. Additionally, these motion-prediction methods are applied for motion control, collision-avoidance planning, and route optimization. Key issues are summarized regarding ship-motion prediction, including online modeling of motion models, real ship validation, and consistency in modeling, optimization, and control. Future technology trends are predicted in mechanism-data fusion modeling, large-scale model, multi-objective motion prediction, etc.

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Section: Ship Extreme Short-term Motion Predictionmentioning

confidence: 99%

A Review on Motion Prediction for Intelligent Ship Navigation

Zhang,

Chu,

Liu

et al. 2024

JMSE

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“…Digital twins of energy assets 13 [9][10][11][12][13][14][15][16][17][18][19][20][21] Energy forecasting 14 [1,[22][23][24][25][26][27][28][29][30][31][32][33][34] Optimization and coordination 18 [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52] VPP applications in smart grids Energy services delivery 31 [4,5,22,35,38, Local energy autonomy 21 [5,…”

Section: Vpp Concepts and Technologymentioning

confidence: 99%

“…Finally, the prediction processes can be joined with the characteristics of energy assets and communities determined using physics-informed DT models [34]. They are implemented using big data infrastructure to consider the energy meters' data as well as a significant number of energy assets in VPP management and operation.…”

Section: Energy Forecastingmentioning

confidence: 99%

Virtual Power Plant Optimization in Smart Grids: A Narrative Review

2022

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Virtual power plants (VPPs) are promising solutions to address the decarbonization and energy efficiency goals in the smart energy grid. They assume the coordination of local energy resources such as energy generation, storage, and consumption. They are used to tackle problems brought by the stochastic nature of renewable energy, lack of energy storage devices, or insufficient local energy flexibility on the demand side. VPP modeling, management, and optimization are open to research problems that should consider, on one side, the local constraints in the operation of the energy resources and power flows and the energy grid’s sustainability objectives on the other side. There are multiple goals to create a VPP, such as to deliver energy services on a market or to the grid operator, to operate a microgrid in autonomy decoupled from the main grid, or to sustain local energy communities. In this paper, we present the results of a narrative review carried out on the domain of VPP optimization for the local energy grid integration. We have defined a search strategy that considers highly rated international databases (i.e., Elsevier, IEEE, and MDPI) in a six-year timeframe and applied objective inclusion/exclusion criteria for selecting articles and publications for the review; 95 articles have been analyzed and classified according to their objectives and solutions proposed for optimizing VPP integration in smart grids. The results of the study show that VPP concepts and applications are well addressed in the research literature, however, there is still work to be done on: engaging prosumers and citizens in such a virtual organization, developing heuristics to consider a wider range of local and global constraints and non-energy vectors, and to decentralize and make transparent the services delivery and financial settlement towards community members. This study can help researchers to understand the current directions for VPP integration in smart grids. As a next step we plan to further analyze the open research directions related to this problem and target the development of innovative solutions to allow the integration of multi-energy assets and management of cross energy sector services in energy communities.

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“…The authors in [307] propose physics-guided NNs that solve PDEs while satisfying thermodynamical constraints. Further applications in differential equation and dynamics modeling are given for example in [45,147,586,587,475,828,853,765,766,230,346,345,807].…”

Section: Knowledge Integration Via Auxiliary Lossesmentioning

confidence: 99%

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

Wörmann¹,

Bogdoll²,

Bührle³

et al. 2022

Preprint

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The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving.Acknowledgement: The research leading to these results is funded by the German Federal Ministry for Economic Affairs and Climate Action within the project "KI Wissen -Entwicklung von Methoden für die Einbindung von Wissen in maschinelles Lernen". The authors would like to thank the consortium for the successful cooperation.

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A Physics-Informed Neural Network for the Prediction of Unmanned Surface Vehicle Dynamics

Cited by 18 publications

References 13 publications

A Review on Motion Prediction for Intelligent Ship Navigation

A Review on Motion Prediction for Intelligent Ship Navigation

Virtual Power Plant Optimization in Smart Grids: A Narrative Review

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

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