A Bayesian approach to the quantification of extremal responses in simulated dynamic structures

Astfalck, Lachlan; Cripps, Edward; Hodkiewicz, Melinda; Milne, Ian

doi:10.1016/j.oceaneng.2019.04.035

Cited by 8 publications

(5 citation statements)

References 28 publications

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“…The model produced through GP regression is probabilistic; thus, it has both universality and solvability. The GP has been used in various studies [53][54][55][56][57][58]. However, these kernel-based modeling approaches often lack a strong theoretical underpinning, due to the absence of rational physical laws [56,57].…”

Section: Related Workmentioning

confidence: 99%

GBM-ILM: Grey-Box Modeling Based on Incremental Learning and Mechanism for Unmanned Surface Vehicles

Zhang,

Li,

Xiong

et al. 2024

JMSE

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Unmanned surface vehicles (USVs) have garnered significant attention across various application fields. A sufficiently accurate kinetic model is essential for achieving high-performance navigation and control of USVs. However, time-varying unobservable internal states and external disturbances pose challenges in accurately modeling the USV’s kinetics, and existing methods face difficulties in accurately estimating unknown time-varying disturbances online while ensuring precise mechanism modeling. To address this issue, a novel grey-box modeling method based on incremental learning and mechanisms (GBM-ILM) is proposed. Its union structure combines the advantages of both incremental learning networks and physical mechanisms for estimating the USV’s full kinetics. Depending on the linear parameter-varying (LPV) mechanism, it not only adheres to physical laws but also calculates the unstructured model errors. An incremental learning network is implemented to continuously refine model errors, by accounting for the USV’s time-varying characteristics and iteratively updating the network parameters and structures to adapt to different USV states and environmental disturbances. To validate this method, we developed the ‘Salmon’ USV and conducted identification experiments in a lake. Compared to tests of other state-of-the-art methods, our method has better adaptability, with 46.34%, 14.86%, and 6.87% accuracy improvements when estimating the USV’s forward, turning, and sideslip dynamic model, respectively.

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Section: Related Workmentioning

confidence: 99%

GBM-ILM: Grey-Box Modeling Based on Incremental Learning and Mechanism for Unmanned Surface Vehicles

Zhang,

Li,

Xiong

et al. 2024

JMSE

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show abstract

“…On the other hand, in recent years, Bayesian methods have been applied in parameters estimation in many fields due to the applicability of Bayesian inference methods [22][23][24][25]. Since Bayesian methods update parameters in the results of observations, they give a formal interpretation as an inductive method and are statistically superior parameter estimation methods.…”

Section: Introductionmentioning

confidence: 99%

MMG 3DOF model identification with uncertainty of observation and hydrodynamic maneuvering coefficients using MCMC method

Mitsuyuki,

Kuribayashi,

Fernandez

et al. 2024

J Mar Sci Technol

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The trajectory prediction using ship maneuverability mathematical models is one of the essential technologies implemented in autonomous surface ship. Several ship maneuverability mathematical models and each one with a particular hydrodynamic coefficient approximation using towing tank tests are existed. However, it is presented difficult to directly inverse estimate the hydrodynamic maneuvering coefficients of a ship maneuverability mathematical model from operational data consisting of ship trajectory and maneuvering operation records. This paper proposed a method for estimating the hydrodynamic maneuvering coefficients of the MMG 3DOF model using three types of time-series ship motions (surge, sway, and yaw velocity) as observed data. In the assumption of this paper, there is uncertainty in observations and the hydrodynamic maneuvering coefficients of the MMG 3DOF model. The proposed method outputs samples of the simultaneous posterior probability distribution of the hydrodynamic maneuvering coefficients by the MCMC method using the observed data and stochastic model. A robust trajectory with a wide range can be presented by conducting ship maneuvering simulations using these samples. To verify the feasibility of the proposed method, this paper conducted observation system simulation experiments (OSSE) using the KVLCC2 L7 model and applied the proposed method to several free-running model ship tests. Results showed that on the assumption that MMG 3DOF model can explain the ship's state and trajectory in real world, the proposed method can estimate the ship hydrodynamic maneuvering coefficients of the MMG 3DOF model corresponding to the observed ship trajectory and control data including the error of observed data.

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“…Numerical models represent the underlying physics of a system and calculate the propagation of the physical states, data-only models find structure in the observed data that may be used to predict future states, and hybrid models incorporate both numerical and data-only techniques. With the increasing volume of data that are being measured, and the development of technologies that allow rapid propagation of uncertainty through computationally demanding maritime models such as statistical emulation (Astfalck et al, 2019b) and digital twins (Ward et al, 2021; Jorgensen et al, 2023), the value of probabilistic forecasting in engineering operations is being recognized (see, for instance, Pinson, 2013 and Anderson Loake et al, 2022). In practice, there is often a plurality of competing forecasts, often at the expense of paying third-party contractors or maintaining measurement equipment to record data required by statistical or machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

Evaluating probabilistic forecasts for maritime engineering operations

Astfalck

Bertolacci

Cripps

2023

DCE

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Maritime engineering relies on model forecasts for many different processes, including meteorological and oceanographic forcings, structural responses, and energy demands. Understanding the performance and evaluation of such forecasting models is crucial in instilling reliability in maritime operations. Evaluation metrics that assess the point accuracy of the forecast (such as root-mean-squared error) are commonplace, but with the increased uptake of probabilistic forecasting methods such evaluation metrics may not consider the full forecasting distribution. The statistical theory of proper scoring rules provides a framework in which to score and compare competing probabilistic forecasts, but it is seldom appealed to in applications. This translational paper presents the underlying theory and principles of proper scoring rules, develops a simple panel of rules that may be used to robustly evaluate the performance of competing probabilistic forecasts, and demonstrates this with an application to forecasting surface winds at an asset on Australia’s North–West Shelf. Where appropriate, we relate the statistical theory to common requirements by maritime engineering industry. The case study is from a body of work that was undertaken to quantify the value resulting from an operational forecasting product and is a clear demonstration of the downstream impacts that statistical and data science methods can have in maritime engineering operations.

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A Bayesian approach to the quantification of extremal responses in simulated dynamic structures

Cited by 8 publications

References 28 publications

GBM-ILM: Grey-Box Modeling Based on Incremental Learning and Mechanism for Unmanned Surface Vehicles

GBM-ILM: Grey-Box Modeling Based on Incremental Learning and Mechanism for Unmanned Surface Vehicles

MMG 3DOF model identification with uncertainty of observation and hydrodynamic maneuvering coefficients using MCMC method

Evaluating probabilistic forecasts for maritime engineering operations

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