Symbolic regression (SR) is a machine learning-based regression method based on genetic
programming principles that integrates techniques and processes from heterogeneous …

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 …

Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …

Carbon emission (CE) has led to increasingly severe climate problems. The key to reducing
CE is to identify the dominant influencing factors and explore their influence degree. The CE …

Porous particles are crucial in environmental engineering, energy, and the chemical
industry, boasting unique hierarchical structures and extensive surface areas. This review …

Porous particles are commonly used in chemical industries and the existence of porous
structures as well as porous-fluid interfaces can essentially affect flow characteristics of …

In this paper, we develop a deterministic drag model for stationary spherical particles in a
Stokes flow using a cascade of data-driven approaches. The model accounts for the …

Abstract Open Porous Media (OPM) Flow is an open-source reservoir simulator used for
solving subsurface porous media flow problems. Focus is placed here on carbon …

Polybrominated diphenyl ethers (PBDEs) are halogenated organic compounds that are
among the major pollutants of water, and there is an urgent need for their removal. This work …

Porous particles with different shapes are commonly used in chemical reactors. Flow
characteristics and drag coefficients of these porous particles, affecting heat and mass …