用机器学习重新发现轨道力学
Rediscovering orbital mechanics with machine learning
摘要：
我们提出了一种方法，使用机器学习自动发现的控制方程和隐藏的属性的真实物理系统的观察。我们训练一个“图形神经网络”，从30年的轨迹数据中模拟我们太阳系的太阳、行星和大卫星的动态。然后，我们使用符号回归来发现神经网络隐式学习的力定律的解析表达式，我们的结果表明它相当于牛顿万有引力定律。所需的关键假设是平移和旋转等方差，以及牛顿第二和第三运动定律。我们的方法正确地发现了符号力定律的形式。此外，我们的方法不需要任何关于行星和卫星质量或物理常数的假设。它们也是通过我们的方法准确推断出来的。当然，尽管经典引力定律自艾萨克·牛顿以来就已为人所知，但我们的结果验证了我们的方法可以从观测数据中发现未知的定律和隐藏的性质。更广泛地说，这项工作代表了实现机器学习加速科学发现潜力的关键一步。
英文摘要：
We present an approach for using machine learning to automatically discover the governing equations and hidden properties of real physical systems from observations. We train a "graph neural network" to simulate the dynamics of our solar system's Sun, planets, and large moons from 30 years of trajectory data. We then use symbolic regression to discover an analytical expression for the force law implicitly learned by the neural network, which our results showed is equivalent to Newton's law of gravitation. The key assumptions that were required were translational and rotational equivariance, and Newton's second and third laws of motion. Our approach correctly discovered the form of the symbolic force law. Furthermore, our approach did not require any assumptions about the masses of planets and moons or physical constants. They, too, were accurately inferred through our methods. Though, of course, the classical law of gravitation has been known since Isaac Newton, our result serves as a validation that our method can discover unknown laws and hidden properties from observed data. More broadly this work represents a key step toward realizing the potential of machine learning for accelerating scientific discovery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
