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新型神经网络可以用快1亿倍速度解决三体问题


本文摘要: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.三体问题是物理中非常简单的数学计算题之一,但它在人工智能技术行业有可能遇到了输了:一种新式神经元网络将来可能以比目前技术性慢一亿倍的速率寻找其解决方法。

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.三体问题是物理中非常简单的数学计算题之一,但它在人工智能技术行业有可能遇到了输了:一种新式神经元网络将来可能以比目前技术性慢一亿倍的速率寻找其解决方法。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·哥白尼爵士舞年所明确指出的,它所说的是不明三个物件最开始的方向和速率,推算出来他们在彼此之间万有引力定律具有下的运动规律,比如地球上、月球表面和太阳光。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.这个问题最开始听得一起有可能很比较简单,但从而造成的焦虑健身运动早就并发症了一位数学家和科学家几百年,以致于除开最专心致志的人之外,别人都尽量减少去要想这个问题。

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Thats 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.这就是为啥推论水上方向时,对比星星和月亮,天文钟更为受欢迎,因为它不那麼出乎意外。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.现如今在科学研究黑洞双星怎样与单独黑洞相互影响,及其宇宙空间中最基础的一些物件怎样相互影响的难题上,三体问题是在其中的最重要构成部分。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.这类神经元网络是由美国爱丁堡学校、牛津大学、西班牙阿威罗高校和西班牙莱顿大学的科学研究工作人员制做的。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.该精英团队产品研发了一种深层人工神经网络(ANN),它以目前的三体问题数据库查询和科学研究工作人员投票表决的用心制定的解决方法来进行训炼。

人工神经网络被确认将来可能比大家目前的方式更为慢下结论精准的回答。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.科学研究工作人员在毕业论文中提到:“经过训练的人工神经网络能够替代目前的标值打法器,使比较慢可扩展的多体系统模拟论述尚需解决困难的状况,如黑洞双星系统的组成及其聚集星团关键塌缩的原因。


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