Interpretation of a minimal neural network to learn the metal-insulator transition in the dynamical mean-field theory
This work is a collaborative work.
Recent Posts
Machine learning prediction of metal-insulator transition in DMFT Hubbard model
In this research, we aim to investigate the relationship between the metal-insulator transition andthe discrete bath orbitals calculated in the dynamical mea...
Self-learning Monte Carlo method with Plaquette Ising model
This work is based on https://journals.aps.org/prb/abstract/10.1103/PhysRevB.95.041101. Self-learning update method The local update-Monte Carlo method is th...
Classical Ising model with MCMC
Classical Ising model Classical Ising model is briefly explained in https://aadeliee22.github.io/physics/tsp-presentation/#classical-ising-model. This model ...
Finite harmonic potential with quantum statistics
In normal harmonic potential with $V(x) = kx^2/2$, we have $\psi(x) = h(x)e^{-x^2/2}$ with coefficients of $h(x)$ by \(a_{n+2}=\frac{(2n+1-2E/\hbar\omega)}{(...
Coanda effect
This was presented on May 2019, and I have a video of my presentation. The purpose of this presentation is to introduce the mathematical mechanisms that exp...
Quantum Ising model
This page is for the summary of my presentation on course “Thermal & Statistical Physics”. Actual presentation in here.