新型神經網絡可以用快1億倍速度解決"三體問題"
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.
三體問題是物理學中最復雜的計算題之一，但它在人工智能領域可能遇到了對手：一種新型神經網絡有望以比現有技術快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.
三體問題是由艾薩克·牛頓爵士最先提出的，它指的是已知三個物體最初的位置和速度，計算它們在相互之間萬有引力作用下的運動規律，例如地球、月球和太陽。
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.
這個問題最初聽起來可能很簡單，但由此產生的混亂運動已經困擾了數學家和物理學家數百年，以至于除了最專注的人以外，其他人都盡量避免去想這個問題。
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海上位置時，比起月亮和星星，天文鐘更受歡迎，因為它不那么令人費解。
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題上，三體問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.
這種神經網絡是由英國愛丁堡大學、劍橋大學、葡萄牙阿威羅大學和荷蘭萊頓大學的研究人員制作的。
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.
該團隊開發了一種深度人工神經網絡（ANN），它以現有的三體問題數據庫和研究人員選出的精心制定的解決方案來進行訓練。人工神經網絡被證實有望比我們現有的方法更快得出準確的答案。
"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：“訓練有素的人工神經網絡可以取代現有的數值求解器，使快速可擴展的多體模擬系統闡明尚待解決的現象，如黑洞雙星系統的形成以及密集星團核心坍縮的起因。”
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.
三體問題是物理學中最復雜的計算題之一，但它在人工智能領域可能遇到了對手：一種新型神經網絡有望以比現有技術快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.
三體問題是由艾薩克·牛頓爵士最先提出的，它指的是已知三個物體最初的位置和速度，計算它們在相互之間萬有引力作用下的運動規律，例如地球、月球和太陽。
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.
這個問題最初聽起來可能很簡單，但由此產生的混亂運動已經困擾了數學家和物理學家數百年，以至于除了最專注的人以外，其他人都盡量避免去想這個問題。
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海上位置時，比起月亮和星星，天文鐘更受歡迎，因為它不那么令人費解。
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題上，三體問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.
這種神經網絡是由英國愛丁堡大學、劍橋大學、葡萄牙阿威羅大學和荷蘭萊頓大學的研究人員制作的。
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.
該團隊開發了一種深度人工神經網絡（ANN），它以現有的三體問題數據庫和研究人員選出的精心制定的解決方案來進行訓練。人工神經網絡被證實有望比我們現有的方法更快得出準確的答案。
"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：“訓練有素的人工神經網絡可以取代現有的數值求解器，使快速可擴展的多體模擬系統闡明尚待解決的現象，如黑洞雙星系統的形成以及密集星團核心坍縮的起因。”
