@article{discovery10195423,
            note = {Crown Copyright {\copyright} 2024 Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).},
       publisher = {Elsevier BV},
         journal = {International Journal of Multiphase Flow},
            year = {2024},
           title = {Machine learning and physics-driven modelling and simulation of multiphase systems},
          volume = {179},
           month = {September},
            issn = {0301-9322},
        keywords = {Machine Learning, 
Numerical simulations, 
Multiphase, 
Multi-fidelity, 
Microfluidics, 
Hybrid methods},
        abstract = {We highlight the work of a multi-university collaborative programme, PREMIERE (PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems), which is at the intersection of multi-physics and machine learning, aiming to enhance predictive capabilities in complex multiphase flow systems across diverse length and time scales. Our contributions encompass a variety of approaches, including the Design of Experiments for nanoparticle synthesis optimisation, Generalised Latent Assimilation models for drop coalescence prediction, Bayesian regularised artificial neural networks, eXtreme Gradient Boosting for microdroplet formation prediction, and a sub-sampling based adversarial neural network for predicting slug flow behaviour in two-phase pipe flows. Additionally, we introduce a generalised latent assimilation technique, Long Short-Term Memory networks for sequence forecasting mixing performance in stirred and static mixers, active learning via Bayesian optimisation to recover coalescence model parameters for high current density electrolysers, Gaussian process regression for drop size distribution predictions for sprays, and acoustic emission signal inversion using gradient boosting machines to characterise particle size distribution in fluidised beds. We also offer perspectives on the development of a shape optimisation framework that leverages the use of a multi-fidelity multiphase emulator. The results presented have applications in chemical synthesis, microfluidics, product manufacturing, and green hydrogen generation.},
             url = {https://doi.org/10.1016/j.ijmultiphaseflow.2024.104936},
          author = {Basha, Nausheen and Arcucci, Rossella and Angeli, Panagiota and Anastasiou, Charitos and Abadie, Thomas and Casas, C{\'e}sar Quilodr{\'a}n and Chen, Jianhua and Cheng, Sibo and Chagot, Lo{\"i}c and Galvanin, Federico and Heaney, Claire E and Hossein, Fria and Hu, Jinwei and Kovalchuk, Nina and Kalli, Maria and Kahouadji, Lyes and Kerhouant, Morgan and Lavino, Alessio and Liang, Fuyue and Nathanael, Konstantia and Magri, Luca and Lettieri, Paola and Materazzi, Massimiliano and Erigo, Matteo and Pico, Paula and Pain, Christopher C and Shams, Mosayeb and Simmons, Mark and Traverso, Tullio and Valdes, Juan Pablo and Wolffs, Zef and Zhu, Kewei and Zhuang, Yilin and Matar, Omar K}
}