This paper explores the evolution of geoscientific inquiry, tracing the progression from
traditional physics-based models to modern data-driven approaches facilitated by significant …

The computerized simulations of physical and socio-economic systems have proliferated in
the past decade, at the same time, the capability to develop high-fidelity system predictive …

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …

Remaining useful life (RUL) prediction, known as 'prognostics', has long been recognized as
one of the key technologies in prognostics and health management (PHM) to maintain the …

Recent work in scientific machine learning has developed so-called physics-informed neural
network (PINN) models. The typical approach is to incorporate physical domain knowledge …

Recent advances of data-driven machine learning have revolutionized fields like computer
vision, reinforcement learning, and many scientific and engineering domains. In many real …

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

There is great interest in how the growth of artificial intelligence and machine learning may
affect global GHG emissions. However, such emissions impacts remain uncertain, owing in …

Conventional damage detection techniques are gradually being replaced by state-of-the-art
smart monitoring and decision-making solutions. Near real-time and online damage …

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