新型神經(jīng)網(wǎng)絡(luò)，助力黑洞研究

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.

三體問(wèn)題是物理學(xué)中最復(fù)雜的計(jì)算題之一，但它在人工智能領(lǐng)域可能遇到了對(duì)手：一種新型神經(jīng)網(wǎng)絡(luò)有望以比現(xiàn)有技術(shù)快1億倍的速度找出其解決方案。

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.

三體問(wèn)題是由艾薩克·牛頓爵士最先提出的，它指的是已知三個(gè)物體最初的位置和速度，計(jì)算它們?cè)谙嗷ブg萬(wàn)有引力作用下的運(yùn)動(dòng)規(guī)律，例如地球、月球和太陽(yáng)。

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.

這個(gè)問(wèn)題最初聽起來(lái)可能很簡(jiǎn)單，但由此產(chǎn)生的混亂運(yùn)動(dòng)已經(jīng)困擾了數(shù)學(xué)家和物理學(xué)家數(shù)百年，以至于除了最專注的人以外，其他人都盡量避免去想這個(gè)問(wèn)題。

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.

這就是為什么在推測(cè)海上位置時(shí)，比起月亮和星星，天文鐘更受歡迎，因?yàn)樗荒敲戳钊速M(fèi)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.

如今在研究黑洞雙星如何與單個(gè)黑洞相互作用，以及宇宙中最基本的一些物體如何相互作用的問(wèn)題上，三體問(wèn)題是其中的重要組成部分。

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.

這種神經(jīng)網(wǎng)絡(luò)是由英國(guó)愛(ài)丁堡大學(xué)、劍橋大學(xué)、葡萄牙阿威羅大學(xué)和荷蘭萊頓大學(xué)的研究人員制作的。

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.

該團(tuán)隊(duì)開發(fā)了一種深度人工神經(jīng)網(wǎng)絡(luò)(ANN)，它以現(xiàn)有的三體問(wèn)題數(shù)據(jù)庫(kù)和研究人員選出的精心制定的解決方案來(lái)進(jìn)行訓(xùn)練。人工神經(jīng)網(wǎng)絡(luò)被證實(shí)有望比我們現(xiàn)有的方法更快得出準(zhǔn)確的答案。

"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.

研究人員在論文中寫道：“訓(xùn)練有素的人工神經(jīng)網(wǎng)絡(luò)可以取代現(xiàn)有的數(shù)值求解器，使快速可擴(kuò)展的多體模擬系統(tǒng)闡明尚待解決的現(xiàn)象，如黑洞雙星系統(tǒng)的形成以及密集星團(tuán)核心坍縮的起因。”