Artificial intelligence: A powerful paradigm for scientific research
Abstrak
Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.
Topik & Kata Kunci
Penulis (47)
Yongjun Xu
Qi Wang
Zhulin An
Fei Wang
Libo Zhang
Yanjun Wu
Fengliang Dong
Cheng-Wei Qiu
Xin Liu
Junjun Qiu
K. Hua
Wentao Su
Huiyu Xu
Yong Han
Xinya Cao
En-ju Liu
C. Fu
Zhigang Yin
Miao Liu
R. Roepman
S. Dietmann
M. Virta
F. Kengara
Changping Huang
Ze Zhang
Lifu Zhang
Taolan Zhao
Jianwei Dai
Jialiang Yang
L. Lan
Ming Luo
Tao Huang
Zhaofeng Liu
Sen Qian
T. An
Xingchen Liu
Bin Zhang
Xiaolei He
Shan Cong
Xiaohong Liu
Wei Zhang
Fang Wang
Chuan‐Qi Lu
Zhipeng Cai
James P. Lewis
J. Tiedje
Jiabao Zhang
Akses Cepat
- Tahun Terbit
- 2021
- Bahasa
- en
- Total Sitasi
- 1356×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1016/j.xinn.2021.100179
- Akses
- Open Access ✓