Machine-Learning Methods for Computational Science and Engineering

Abstract

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 scientific computing, as well as in data-mining and processing. In this paper, we provide a review of the state-of-the-art in ML for computational science and engineering. We discuss ways of using ML to speed up or improve the quality of simulation techniques such as computational fluid dynamics, molecular dynamics, and structural analysis. We explore the ability of ML to produce computationally efficient surrogate models of physical applications that circumvent the need for the more expensive simulation techniques entirely. We also discuss how ML can be used to process large amounts of data, using as examples many different scientific fields, such as engineering, medicine, astronomy and computing. Finally, we review how ML has been used to create more realistic and responsive virtual reality applications.

Enhancing Clinical Translation of Cancer Using Nanoinformatics

“…Extracting meaningful features of scientific value from the data is not always easy and can occasionally be as time-consuming as the experiments or computations providing them. Hence, sophisticated visualization techniques, signal processing algorithms, and statistics are essential for this task [95].…”

Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution

“…Given the abundance of data and data‐driven tools, the development of ROMs is gaining increasing popularity nowadays. As highlighted in our introduction, there are a great number of review articles available concerning various aspects of model order reduction and its applications [23,29,37,38,44,45,52, 54‐56,67,71,95,105,106,118,165,169,183,202,204,210,220,224,225,245,262,278,287,317‐319,328,344,351,356,358,364], and more relevant to our discussion, the enabling role of model order reduction approaches in developing next generation DT systems has been also discussed [136,277]. Of particular interest, MPC [113,139,218] originated in the late seventies and has since then evolved considerably.…”

“…In this perspective letter, we aim to provide an overview of HAM strategies relevant to scientific and engineering applications. The topic spans a wide spectrum, and there are a great number of review articles on relevant discussions, methodologies, and applications [23,29,37,38,44,45,52,54‐56,67,71,95,105,106,118,165,169,183,202,204,210,220,224,225,245,262,278,287,317‐319,328,344, 351,356,358,364]. Therefore, it is not our intention to give a complete biography, but rather somehow present our subjective perspectives with an emphasis on the emerging methodologies and enabling technologies from modeling perspectives.…”

AR Guidance System for Traffic Circumvention and Collision Avoidance: Emergency Services Case Study

“…The AI system will be further trained through previous accident data provided by the local police department. Also, the VR simulations performed by different volunteer drivers in the VR Driving Simulator will provide multiple options for collision avoidance manoeuvres and their suitability based on traffic, weather and accident scenarios [24][25][26].…”