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 …

Gas-solid fluidization technology has been commercialized in many industrial applications
since its implementation in the fluid catalytic cracking process in the early 1940s, however …

Finding accurate solutions to partial differential equations (PDEs) is a crucial task in all
scientific and engineering disciplines. It has recently been shown that machine learning …

The re-kindled fascination in machine learning (ML), observed over the last few decades,
has also percolated into natural sciences and engineering. ML algorithms are now used in …

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

High-fidelity reconstruction of dynamic fluids from sparse multiview RGB videos remains a
formidable challenge, due to the complexity of the underlying physics as well as the severe …

The significant growth of artificial intelligence (AI) methods in machine learning (ML) and
deep learning (DL) has opened opportunities for fluid dynamics and its applications in …

In this paper, we aim to model 3D scene dynamics from multi-view videos. Unlike the
majority of existing works which usually focus on the common task of novel view synthesis …

Multiphase microfluidics enables an alternative approach with many possibilities in studying,
analyzing, and manufacturing functional materials due to its numerous benefits over …

The main objective of the present study is to introduce two novel benchmark solutions
namely: two-dimensional three-fluid Rayleigh-Taylor Instability problems, aiming to provide …