Abstract Machine learning is rapidly becoming a core technology for scientific computing,
with numerous opportunities to advance the field of computational fluid dynamics. Here we …

Physics-informed machine-learning (PIML) enables the integration of domain knowledge
with machine learning (ML) algorithms, which results in higher data efficiency and more …

Physics-informed neural networks (PINNs) are successful machine-learning methods for the
solution and identification of partial differential equations. We employ PINNs for solving the …

The field of machine learning (ML) has rapidly advanced the state of the art in many fields of
science and engineering, including experimental fluid dynamics, which is one of the original …

This review covers the new developments in machine learning (ML) that are impacting the
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …

Biomass-derived materials (BDM) have broad applications in water and agricultural
systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to …

High-fidelity models of multiphysics fluid flow processes are often computationally
expensive. On the other hand, less accurate low-fidelity models could be efficiently executed …

The incorporation of physical information in machine learning frameworks is opening and
transforming many application domains. Here the learning process is augmented through …

The imminent impact of immersive technologies in society urges for active research in real-
time and interactive physics simulation for virtual worlds to be realistic. In this context …

Physics-informed neural networks (PINN) are machine-learning methods that have been
proved to be very successful and effective for solving governing equations of fluid flow. In …