EAGER: ADAPT: AI-Enhanced Sampling for Lattice Field Theory and Beyond

该奖项将使用人工智能极大地加快基本粒子物理学的理论计算。目前控制基本粒子相互作用的详细的非线性方程不能直接求解，因此使用一种称为格子场理论的计算技术在空间(晶格)的不同点上近似求解它们，然后组合得到近似结果。所能获得的精度仅受计算时间的限制。目前，世界上最大的高性能计算机都在忙于格子场论方程的求解。这项工作将应用一种名为归一化流动的新技术，以大大减少这些计算所需的计算时间。归一化流动是一类深度生成模型，可以有效地模拟复杂的高维分布，并有可能改变物理学的许多领域。这些模型是通过组成许多简单的可逆神经网络层来设计的，这些层旨在有效地计算期望的结果。这些流可以对来自真实实验的数据的概率分布进行建模，从而只将计算时间花在将主导所需解决方案的计算部分上。这一奖项将为格子场理论发展这些归一化流动。这项工作还将加强纽约公立学校系统的公共宣传，并保持活跃的人工智能社交媒体存在。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award will use Artificial Intelligence to greatly speed up theoretical calculations in elementary particle physics. Currently the detailed non-linear equations governing the interactions of elementary particles are not directly solvable, so a computational technique called Lattice Field Theory is used to approximately solve them at separate points in space (the lattice) and then combined to get an approximate result. The precision which can be obtained is only limited by computational time. Currently the largest High-Performance Computers in the world are kept busy with Lattice Field Theory equation solving. This work will apply a new technique called Normalizing Flows to greatly reduce the required compute time for these calculations. Normalizing flows are a class of deep generative models that can effectively model complex, high-dimensional distributions and have the potential to transform many areas of physics. These models are designed by composing many simple invertible neural network layers designed to efficiently compute the desired result. These flows can model the probability distribution of data from a real experiment and thus spend computational time only on the parts of the calculation that will dominate the required solution. This award will develop these normalizing flows for lattice field theory. The work will also enhance public outreach in the New York Public School system and maintain an active Artificial Intelligence social media presence.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.