Eliška Greplová (1/3) "Discovering physics without neural networks" скачать в хорошем качестве

Eliška Greplová (1/3) "Discovering physics without neural networks"

Summer school: Machine Learning in Quantum Physics and Chemistry, 24.08-3.09.2021, Warsaw Abstract: Unsupervised learning without neural networks can be surprisingly powerful and can help us get lots of physics insight. In this lecture, I will introduce some basic clustering tools: principal component analysis (PCA), kernel PCA, and t-Neighbour Stochastic Embedding (t-SNE). I will illustrate each of these with a quantum physics example. We will see that even simple clustering methods can help us identify phase transitions in the spin systems without knowing any underlying theory. We will also explore the boundary of applicability of such methods. Using t-SNE we will analyze some completely new experimental data from bilayer graphene quantum dots and learn to reconstruct the quantum dot wave functions.