| Physics-based shaft power prediction for large merchant ships using neural networks AI Parkes, AJ Sobey, DA Hudson Ocean Engineering 166, 92-104, 2018 | 55 | 2018 |
| Power prediction for a vessel without recorded data using data fusion from a fleet of vessels AI Parkes, TD Savasta, AJ Sobey, DA Hudson Expert Systems with Applications 187, 115971, 2022 | 5 | 2022 |
| Efficient vessel power prediction in operational conditions using machine learning AI Parkes, TD Savasta, AJ Sobey, DA Hudson Practical Design of Ships and Other Floating Structures, 350-367, 2019 | 4 | 2019 |
| The importance of error measures for machine learning regression to approximate the ground truth A Parkes University of Southampton, 2021 | 1 | 2021 |
| Automation for Interpretable Machine Learning Through a Comparison of Loss Functions to Regularisers AI Parkes, J Camilleri, DA Hudson, AJ Sobey arXiv preprint arXiv:2106.03428, 2021 | | 2021 |
| Towards Error Measures which Influence a Learners Inductive Bias to the Ground Truth AI Parkes, AJ Sobey, DA Hudson arXiv preprint arXiv:2105.01567, 2021 | | 2021 |
| Exploring Measures for Better Physical Representation in Black Box Models AI Parkes, AJ Sobey, D Hudson Available at SSRN 4895453, 0 | | |
