Technological advancements have substantially increased computational power
and data availability, enabling the application of powerful machine-learning
(ML) techniques across various fields. However, our ability to leverage ML
methods for scientific discovery, {\it i.e.} to obtain fundamental and
formalized knowledge about natural processes, is still in its infancy. In this
review, we explore how the scientific community can increasingly leverage ML
techniques to achieve scientific discoveries. We observe that the applicability
and opportunity of ML depends strongly on the nature of the problem domain, and
whether we have full ({\it e.g.}, turbulence), partial ({\it e.g.},
computational biochemistry), or no ({\it e.g.}, neuroscience) {\it a-priori}
knowledge about the governing equations and physical properties of the system.
Although challenges remain, principled use of ML is opening up new avenues for
fundamental scientific discoveries. Throughout these diverse fields, there is a
theme that ML is enabling researchers to embrace complexity in observational
data that was previously intractable to classic analysis and numerical
investigations.

科学界利用机器学习技术进行科学探索的能力正在初生阶段，然而，机器学习的原理应用正在为基础科学发现开辟新的途径，尤其在处理观测数据的复杂性方面。