论文标题
使用可区分的动力学模拟和最大的熵损失函数，无监督发现惯性融合等离子体物理
Unsupervised Discovery of Inertial-Fusion Plasma Physics using Differentiable Kinetic Simulations and a Maximum Entropy Loss Function
论文作者
论文摘要
等离子体支持集体模式和粒子波相互作用，从而导致惯性融合能量应用中的复杂行为。尽管有时可以将等离子体建模为带电的流体，但动力学描述对于在更高维动量位置相位空间中的非线性效应的研究很有用，该相位描述了血浆动力学的全部复杂性。我们为等离子体动力学3D部分差异方程式创建一个可区分的求解器，并引入特定于域的目标函数。使用此框架，我们对神经网络进行基于梯度的优化，这些神经网络在给定一组初始条件的情况下向可区分的求解器提供强迫函数参数。我们将其应用于惯性融合相关的配置，并发现优化过程利用了以前未被发现的新型物理效应。
Plasma supports collective modes and particle-wave interactions that leads to complex behavior in inertial fusion energy applications. While plasma can sometimes be modeled as a charged fluid, a kinetic description is useful towards the study of nonlinear effects in the higher dimensional momentum-position phase-space that describes the full complexity of plasma dynamics. We create a differentiable solver for the plasma kinetics 3D partial-differential-equation and introduce a domain-specific objective function. Using this framework, we perform gradient-based optimization of neural networks that provide forcing function parameters to the differentiable solver given a set of initial conditions. We apply this to an inertial-fusion relevant configuration and find that the optimization process exploits a novel physical effect that has previously remained undiscovered.
