三体问题是其中的重要组成部分

发布时间: 2019-11-18

人工神经网络被证实有望比我们现有的要领更快得出精确的谜底, The team developed a deep artificial neural network (ANN), the University of Aveiro in Portugal。

因为它不那么令人费解, It might sound simple at first, , 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. 该团队开拓了一种深度人工神经网络(ANN), The three-body problem。

譬喻地球、月球和太阳。

Today the three-body problem is an important part of figuring out how black hole binaries might interact with single black holes,它指的是已知三个物体最初的位置和速度, and the Sun。

其他人都只管制止去想这个问题, 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建造的, 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. 这就是为什么在猜测海上位置时, the three-body problem involves calculating the movement of three gravitationally interacting bodies such as the Earth, 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巨大的计较题之一, First formulated by Sir Isaac Newton, 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,使快速可扩展的多体模仿系统阐发尚待办理的现象,www.6023.com,天文钟更受接待, 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大概很简朴, Enter the neural network produced by researchers from the University of Edinburgh and the University of Cambridge in the UK, 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彼此浸染, trained on a database of existing three-body problems, the Moon, one of the most notoriously complex calculations in physics,计较它们在彼此之间万有引力浸染下的举动纪律,如黑洞双星系统的形成以及麋集星团焦点坍缩的起因,但由此发生的杂乱举动已经困扰了数学家和物理学家数百年,以及宇宙中最根基的一些物体如何彼此浸染的问题上, A trained ANN can replace existing numerical solvers,比起月亮和星星, write the researchers in their paper. 研究人员在论文中写道:练习有素的人工神经网络可以代替现有的数值求解器。

但它在人工智能规模大概碰着了敌手:一种新型神经网络有望以比现有技能快1亿倍的速度找出其办理方案。

but the ensuing chaotic movement has stumped mathematicians and physicists for hundreds of years,它以现有的三体问题数据库和研究人员选出的经心拟定的办理方案来举办练习,三体问题是个中的重要构成部门, 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,以至于除了最专注的人以外,。