G. F. N. Gonçalves et al., “Data-driven surrogate modeling and benchmarking for process equipment,” Data-Centric Eng., vol. 1, pp. 1–19, 2020

DOI: https://doi.org/10.1017/dce.2020.8

G. D. Ranasinghe, T. Lindgren, M. Girolami, and A. K. Parlikad, “A Methodology for Prognostics under the Conditions of Limited Failure Data Availability,” IEEE Access, vol. 7, 2019

DOI: https://doi.org/10.1109/ACCESS.2019.2960310

L. Kahouadji et al., “A numerical investigation of three-dimensional falling liquid films,” Environ. Fluid Mech., vol. 22, no. 2–3, 2022

DOI: https://doi.org/10.1007/s10652-022-09849-2

C. E. Heaney et al., “An AI-based non-intrusive reduced-order model for extended domains applied to multiphase flow in pipes,” Phys. Fluids, vol. 34, no. 5, 2022

DOI: https://doi.org/10.1063/5.0088070

M. Cheng, F. Fang, I. M. Navon, and C. C. Pain, “A real-time flow forecasting with deep convolutional generative adversarial network: Application to flooding event in Denmark,” Phys. Fluids, vol. 33, no. 5, 2021

DOI: https://doi.org/10.1063/5.0051213

T. R. F. Phillips, C. E. Heaney, P. N. Smith, and C. C. Pain, “An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion,” Int. J. Numer. Methods Eng., vol. 122, no. 15, 2021

DOI: https://doi.org/10.1002/nme.6681

S. Paul et al., “Analysis and control of vapor bubble growth inside solid-state nanopores,” J. Therm. Sci. Technol., vol. 16, no. 1, 2021

DOI: https://doi.org/10.1299/jtst.2021jtst0007

F. Hossein, M. Materazzi, P. Lettieri, and P. Angeli, “Application of acoustic techniques to fluid-particle systems – A review,” Chemical Engineering Research and Design, vol. 176. 2021

DOI: https://doi.org/10.1016/j.cherd.2021.09.031

C. E. Heaney, Y. Li, O. K. Matar, and C. C. Pain, “Applying Convolutional Neural Networks to Data on Unstructured Meshes with Space-Filling Curves,” 2020

DOI: http://arxiv.org/abs/2011.14820

M. Kalli, L. Chagot, and P. Angeli, “Comparison of surfactant mass transfer with drop formation times from dynamic interfacial tension measurements in microchannels,” J. Colloid Interface Sci., vol. 605, 2022

DOI: https://doi.org/10.1016/j.jcis.2021.06.178

J. Chen et al., “Computational fluid dynamics simulations of phase separation in dispersed oil-water pipe flows,” Chem. Eng. Sci., vol. 267, p. 118310, 2023

DOI: https://doi.org/10.1016/j.ces.2022.118310

P. K. Inguva, P. J. Walker, H. W. Yew, K. Zhu, A. J. Haslam, and O. K. Matar, “Continuum-scale modelling of polymer blends using the Cahn-Hilliard equation: transport and thermodynamics,” Soft Matter, vol. 17, no. 23. 2021

DOI: https://doi.org/10.1039/D1SM00272D

J. P. Valdés, L. Kahouadji, and O. K. Matar, “Current advances in liquid–liquid mixing in static mixers: A review,” Chemical Engineering Research and Design, vol. 177. 2022

DOI: https://doi.org/10.1016/j.cherd.2021.11.016

C. E. Heaney, Y. Li, and C. C. Pain, Data Assimilation Predictive GAN ( DA-PredGAN ) Applied to a Spatio-Temporal Compartmental Model in Epidemiology, vol. 123. Springer US, 2023

DOI: https://doi.org/10.1007/s10915-022-02078-1

Cheng, M., Fang, F., Pain, C. C., & Navon, I. M. (2020). Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network. Computer Methods in Applied Mechanics and Engineering.

DOI: https://doi.org/10.1016/j.cma.2020.113000

Z. Titus, C. Heaney, C. Jacquemyn, P. Salinas, M. Jackson, and C. Pain, “Conditioning surface-based geological models to well data using artificial neural networks,” Comput. Geosci., vol. 26, no. 4, 2022

DOI: https://doi.org/10.1007/s10596-021-10088-5

C. Buizza et al., “Data Learning: Integrating Data Assimilation and Machine Learning,” J. Comput. Sci., vol. 58, 2022

DOI: https://doi.org/10.1016/j.jocs.2021.101525

Constante-Amores, C. R., Kahouadji, L., Batchvarov, A., Shin, S., Chergui, J., Juric, D., & Matar, O. K. (2021a). Direct numerical simulations of transient turbulent jets: Vortex-interface interactions. Journal of Fluid Mechanics.

DOI: https://doi.org/10.1017/jfm.2021.519

Constante-Amores, C. R., Kahouadji, L., Batchvarov, A., Shin, S., Chergui, J., Juric, D., & Matar, O. K. (2021). Dynamics of a surfactant-laden bubble bursting through an interface. Journal of Fluid Mechanics, 911, 1–10.

DOI: https://doi.org/10.1017/jfm.2020.1099

Constante-Amores, Cristian R., Kahouadji, L., Batchvarov, A., Shin, S., Chergui, J., Juric, D., & Matar, O. K. (2020). Dynamics of retracting surfactant-laden ligaments at intermediate Ohnesorge number. Physical Review Fluids, 5(8), 1–24.

DOI: https://doi.org/10.1103/PhysRevFluids.5.084007

Kovalchuk, N., Alberini, F., & Simmons, M. J. H. (2020). Effect of moderate DC electric field on formation of surfactant-laden drops. Chemical Engineering Research and Design, 157, 133–141.

DOI: https://doi.org/10.1016/j.cherd.2020.03.009

I. Kiratzis, N. M. Kovalchuk, M. J. H. Simmons, and D. Vigolo, “Effect of surfactant addition and viscosity of the continuous phase on flow fields and kinetics of drop formation in a flow-focusing microfluidic device,” Chem. Eng. Sci., vol. 248, 2022

DOI: https://doi.org/10.1016/j.ces.2021.117183

Kovalchuk, N. M., & Simmons, M. J. H. (2021a). Effect of Surfactant Dynamics on Flow Patterns Inside Drops Moving in Rectangular Microfluidic Channels. Colloids and Interfaces, 5(3).

DOI: https://doi.org/10.3390/colloids5030040

T. Kadeethum et al., “Enhancing high-fidelity nonlinear solver with reduced order model,” Sci. Rep., vol. 12, no. 1, pp. 1–15, 2022

DOI: https://doi.org/10.1038/s41598-022-22407-6

Batchvarov, A., Kahouadji, L., Magnini, M., Constante-Amores, C. R., Shin, S., Chergui, J., Juric, D., Craster, R. V., & Matar, O. K. (2020). Effect of surfactant on elongated bubbles in capillary tubes at high Reynolds number. Physical Review Fluids.

DOI: https://doi.org/10.1103/PhysRevFluids.5.093605

Moran, H. R., Magnini, M., Markides, C. N., & Matar, O. K. (2021). Inertial and buoyancy effects on the flow of elongated bubbles in horizontal channels. International Journal of Multiphase Flow.

DOI: https://doi.org/10.1016/j.ijmultiphaseflow.2020.103468

H. Rappel, M. Girolami, and L. A. A. Beex, “Intercorrelated random fields with bounds and the Bayesian identification of their parameters: Application to linear elastic struts and fibers,” Int. J. Numer. Methods Eng., vol. 123, no. 15, 2022

DOI: https://doi.org/10.1002/nme.6974

R. Maulik, T. Botsas, N. Ramachandra, L. R. Mason, and I. Pan, “Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation,” Phys. D Nonlinear Phenom., vol. 416, 2021

DOI: https://doi.org/10.1016/j.physd.2020.132797

H. A. Abubakar and O. K. Matar, “Linear stability analysis of Taylor bubble motion in downward flowing liquids in vertical tubes,” J. Fluid Mech., vol. 941, pp. 1–37, 2022

DOI: https://doi.org/10.1017/jfm.2022.261

G. F. N. Gonçalves and O. K. Matar, “Mechanistic modelling of two-phase slug flows with deposition,” Chem. Eng. Sci., vol. 259, 2022

DOI: https://doi.org/10.1016/j.ces.2022.117796

C. Quilodrán-Casas, V. L. S. Silva, R. Arcucci, C. E. Heaney, Y. K. Guo, and C. C. Pain, “Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic,” Neurocomputing, vol. 470, 2022

DOI: https://doi.org/10.1016/j.neucom.2021.10.043

T. R. F. Phillips et al., “Multi-Output Regression with Generative Adversarial Networks (MOR-GANs),” Appl. Sci., vol. 12, no. 18, 2022

DOI: https://doi.org/10.3390/ app12189209

Inguva, P., Mason, L. R., Pan, I., Hengardi, M., & Matar, O. K. (2020). Numerical simulation, clustering, and prediction of multicomponent polymer precipitation. Data-Centric Engineering.

DOI: https://doi.org/10.1017/dce.2020.14

Farooq, U., Stafford, J., Petit, C., & Matar, O. K. (2020). Numerical simulations of a falling film on the inner surface of a rotating cylinder. Physical Review E, 102(4), 1–18.

DOI: https://doi.org/10.1103/PhysRevE.102.043106

Matar, O. K., & Pain, C. C. (2021). Prediction of multiphase flows with sharp interfaces using anisotropic mesh optimisation. Advances in Engineering Software, 160(March).

DOI: https://doi.org/10.1016/j.advengsoft.2021.103044

F. Liang et al., “Numerical study of oil-water emulsion formation in stirred vessels: effect of impeller speed,” Flow Meas. Instrum., vol. 2, 2022

DOI: https://doi.org/10.1017/flo.2022.27

T. R. F. Phillips, C. E. Heaney, B. S. Tollit, P. N. Smith, and C. C. Pain, “Reduced-order modelling with domain decomposition applied to multi-group neutron transport,” Energies, vol. 14, no. 5, 2021

DOI: https://doi.org/10.3390/en14051369

Constante-Amores, C. R., Kahouadji, L., Batchvarov, A., Shin, S., Chergui, J., Juric, D., & Matar, O. K. (2020). Rico and the jets: Direct numerical simulations of turbulent liquid jets. Physical Review Fluids.

DOI: https://doi.org/10.1103/PhysRevFluids.5.110501

C. E. Heaney, A. G. Buchan, C. C. Pain, and S. Jewer, “Reduced-order modelling applied to the multigroup neutron diffusion equation using a nonlinear interpolation method for control-rod movement,” Energies, vol. 14, no. 5, 2021

DOI: https://doi.org/10.3390/en14051350

E. A. de. R. Chanona, P. Petsagkourakis, E. Bradford, J. E. A. Graciano, and B. Chachuat, “Real-time optimization meets Bayesian optimization and derivative-free optimization: A tale of modifier adaptation,” Comput. Chem. Eng., vol. 147, 2021

DOI: https://doi.org/10.1016/j.compchemeng.2021.107249

C. R. Constante-Amores et al., “Role of surfactant-induced Marangoni stresses in drop-interface coalescence,” J. Fluid Mech., vol. 925, 2021

DOI: https://doi.org/10.1017/jfm.2021.682

M. Cheng et al., “Spatio-Temporal Hourly and Daily Ozone Forecasting in China Using a Hybrid Machine Learning Model: Autoencoder and Generative Adversarial Networks,” J. Adv. Model. Earth Syst., vol. 14, no. 3, 2022

DOI: https://doi.org/10.1029/2021MS002806

L. Chagot et al., “Surfactant-laden droplet size prediction in a flow-focusing microchannel: a data-driven approach,” Lab Chip, p., 2022

DOI: https://doi.org/10.1039/d2lc00416j

R. K. Nazareth, G. Karapetsas, K. Sefiane, O. K. Matar, and P. Valluri, “Stability of slowly evaporating thin liquid films of binary mixtures,” Phys. Rev. Fluids, vol. 5, no. 10, 2020

DOI: https://doi.org/10.1103/PhysRevFluids.5.104007

Kovalchuk, N. M., Sagisaka, M., Osaki, S., & Simmons, M. J. H. (2020). Superspreading performance of branched ionic trimethylsilyl surfactant Mg(AOTSiC)2. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 604(July), 125277.

DOI: https://doi.org/10.1016/j.colsurfa.2020.125277

Kovalchuk, N. M., & Simmons, M. J. H. (2021). Surfactant-mediated wetting and spreading: Recent advances and applications. Current Opinion in Colloid and Interface Science, 51.

DOI: https://doi.org/10.1016/j.cocis.2020.07.004

L. Kahouadji et al., “The transition to aeration in turbulent two-phase mixing in stirred vessels,” Flow, vol. 2, 2022

DOI: https://doi.org/10.1017/flo.2022.24

J. Povala, I. Kazlauskaite, E. Febrianto, F. Cirak, and M. Girolami, “Variational Bayesian approximation of inverse problems using sparse precision matrices,” Comput. Methods Appl. Mech. Eng., vol. 393, 2022

DOI: https://doi.org/10.1016/j.cma.2022.114712

Batchvarov, A., Kahouadji, L., Constante-Amores, C. R., Norões Gonçalves, G. F., Shin, S., Chergui, J., Juric, D., Craster, R. V., & Matar, O. K. (2020). Three-dimensional dynamics of falling films in the presence of insoluble surfactants. Journal of Fluid Mechanics, 1996, 1–10.

DOI: https://doi.org/10.1017/jfm.2020.796

S. H. Hue, L. Chagot, and P. Angeli, “Viscoelastic effects of immiscible liquid-liquid displacement in microchannels with bends,” Phys. Fluids, vol. 34, no. 7, 2022

DOI: https://doi.org/10.1063/5.0091501