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The three-body problem, one of the most notoriously complex calculations in physics, may have met its match in artificial intelligence: a new neural network promises to find solutions up to 100 million times faster than existing techniques.
三体问题是物理学中最复杂的计算题之一，但它在人工智能领域可能遇到了对手：一种新型神经网络有望以比现有技术快1亿倍的速度找出其解决方案。
First formulated by Sir Isaac Newton, the three-body problem involves calculating the movement of three gravitationally interacting bodies – such as the Earth, the Moon, and the Sun, for example – given their initial positions and velocities.
三体问题是由艾萨克·牛顿爵士最先提出的，它指的是已知三个物体最初的位置和速度，计算它们在相互之间万有引力作用下的运动规律，例如地球、月球和太阳。
It might sound simple at first, but the ensuing chaotic movement has stumped mathematicians and physicists for hundreds of years, to the extent that all but the most dedicated humans have tried to avoid thinking about it as much as possible.
这个问题最初听起来可能很简单，但由此产生的混乱运动已经困扰了数学家和物理学家数百年，以至于除了最专注的人以外，其他人都尽量避免去想这个问题。
That's why chronometer time-keepers became more popular for calculating positions at sea rather than using the Moon and the stars – it was just less of a head-scratcher.
这就是为什么在推测海上位置时，比起月亮和星星，天文钟更受欢迎，因为它不那么令人费解。
Today the three-body problem is an important part of figuring out how black hole binaries might interact with single black holes, and from there how some of the most fundamental objects of the Universe interact with each other.
如今在研究黑洞双星如何与单个黑洞相互作用，以及宇宙中最基本的一些物体如何相互作用的问题上，三体问题是其中的重要组成部分。
Enter the neural network produced by researchers from the University of Edinburgh and the University of Cambridge in the UK, the University of Aveiro in Portugal, and Leiden University in the Netherlands.
这种神经网络是由英国爱丁堡大学、剑桥大学、葡萄牙阿威罗大学和荷兰莱顿大学的研究人员制作的。
The team developed a deep artificial neural network (ANN), trained on a database of existing three-body problems, plus a selection of solutions that have already been painstakingly worked out. The ANN was shown to have a lot of promise for reaching accurate answers much more quickly than we can today.
该团队开发了一种深度人工神经网络（ANN），它以现有的三体问题数据库和研究人员选出的精心制定的解决方案来进行训练。人工神经网络被证实有望比我们现有的方法更快得出准确的答案。
"A trained ANN can replace existing numerical solvers, enabling fast and scalable simulations of many-body systems to shed light on outstanding phenomena such as the formation of black-hole binary systems or the origin of the core collapse in dense star clusters," write the researchers in their paper.
研究人员在论文中写道：“训练有素的人工神经网络可以取代现有的数值求解器，使快速可扩展的多体模拟系统阐明尚待解决的现象，如黑洞双星系统的形成以及密集星团核心坍缩的起因。”
