The concept of a digital twin of Earth envisages the convergence of Big Earth Data with
physics-based models in an interactive computational framework that enables monitoring …

The architecture, engineering and construction (AEC) industry is experiencing a
technological revolution driven by booming digitisation and automation. Advances in …

We present the application of a class of deep learning, known as Physics Informed Neural
Networks (PINN), to inversion and surrogate modeling in solid mechanics. We explain how …

We propose a new approach to the solution of the wave propagation and full waveform
inversions (FWIs) based on a recent advance in deep learning called physics‐informed …

Machine learning (ML) has evolved rapidly over recent years with the promise to
substantially alter and enhance the role of data science in a variety of disciplines. Compared …

In this paper, we introduce SciANN, a Python package for scientific computing and physics-
informed deep learning using artificial neural networks. SciANN uses the widely used deep …

Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …

Acoustic data provide scientific and engineering insights in fields ranging from biology and
communications to ocean and Earth science. We survey the recent advances and …

精确获取震相到时是地震定位和地震走时成像等研究的重要基础. 近年来, 随着地震台站的不断
加密, 地震台网监测到的地震数量成倍增长, 发展快速, 准确, 适用性强的震相到时自动拾取算法 …

Population growth, economic development, and rapid urbanization in many areas have led
to increased exposure and vulnerability of structural and infrastructure systems to hazards …