Machine learning (ML) is a collection of methods used to develop understanding and
predictive capability by learning relationships embedded in data. ML methods are becoming …

The confluence of our ability to handle big data, significant increases in instrumentation
density and quality, and rapid advances in machine learning (ML) algorithms have placed …

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 …

Hydraulic fracturing (HF) is a technique that is used for extracting petroleum resources from
impermeable host rocks. In this process, fluid injected under high pressure causes fractures …

BACKGROUND The solid Earth, oceans, and atmosphere together form a complex
interacting geosystem. Processes relevant to understanding Earth's geosystem behavior …

Seismology is a data rich and data-driven science. Application of machine learning for
gaining new insights from seismic data is a rapidly evolving sub-field of seismology. The …

This article provides an overview of current applications of machine learning (ML) in
seismology. ML techniques are becoming increasingly widespread in seismology, with …

The recent evolution of induced seismicity in Central United States calls for exhaustive
catalogs to improve seismic hazard assessment. Over the last decades, the volume of …

The all-pairs-similarity-search (or similarity join) problem has been extensively studied for
text and a handful of other datatypes. However, surprisingly little progress has been made …