| Bayesian sensitivity analysis of a 1D vascular model with Gaussian process emulators A Melis, RH Clayton, A Marzo International Journal for Numerical Methods in Biomedical Engineering 33 (12 …, 2017 | 36 | 2017 |
| Generative deep learning applied to biomechanics: A new augmentation technique for motion capture datasets M Bicer, ATM Phillips, A Melis, AH McGregor, L Modenese Journal of biomechanics 144, 111301, 2022 | 13 | 2022 |
| Improved biomechanical metrics of cerebral vasospasm identified via sensitivity analysis of a 1D cerebral circulation model A Melis, F Moura, I Larrabide, K Janot, RH Clayton, AP Narata, A Marzo Journal of biomechanics 90, 24-32, 2019 | 10 | 2019 |
| An engineering approach towards a more discrete and efficient urinary drainage system A Marzo, A Melis, J Unger, R Sablotni, M Pistis, AD McCarthy Proceedings of the Institution of Mechanical Engineers, Part H: Journal of …, 2019 | 9 | 2019 |
| Gaussian process emulators for 1D vascular models A Melis University of Sheffield, 2017 | 8 | 2017 |
| openBF: Julia software for 1D blood flow modelling A Melis https://figshare.com/articles …, 2018 | 4 | 2018 |
| Deep learning for enlarging human motion capture (MOCAP) datasets M Bicer, ATM Phillips, A Melis, A McGregor, L Modenese Orthopaedic Proceedings 105 (SUPP_16), 63-63, 2023 | 1 | 2023 |
| A MORE EFFICIENT APPROACH TO PERFORM SENSITIVITY ANALYSES IN 0D/1D CARDIOVASCULAR MODELS A Melis, RH Clayton, A Marzo | 1 | |
| Generative Adversarial Networks to Create Synthetic Motion Capture Datasets Including Subject and Gait Characteristics M Bicer, A Phillips, A Melis, AH McGregor, L Modenese Available at SSRN 4717282, 0 | | |
| Use of a Gaussian process emulator and 1D circulation model to characterize cardiovascular pathologies and guide clinical treatment A Melis, RH Clayton, AP Narata, A Mustafa, A Marzo | | |
| rticle A Melis, RH Clayton, A Marzo | | |
Alberto MarzoLecturer, University of SheffieldVerified email at sheffield.ac.uk
