Interpretation of a minimal neural network to learn the metal-insulator transition in the dynamical mean-field theory
This work is a collaborative work.
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2021-2 (fall)
2021-1.5 (summer)
Interpretation of a minimal neural network to learn the metal-insulator transition in the dynamical mean-field theory
This work is a collaborative work.
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...
2021-1 (spring)
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...
2020-2 (fall)
Quantum Ising model
This page is for the summary of my presentation on course “Thermal & Statistical Physics”. Actual presentation in here.
2020-1.5 (summer)
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 ...
2020-1 (spring)
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)}{(...
2019-1 (spring)
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...