Machine Learning applied to solving Nuclear Many-body Problems
Contents
What is this talk about?
More material
Why? Basic motivation
What are the basic ingredients?
Quantum Monte Carlo Motivation
Running the codes
Energy as function of iterations, \( N=2 \) electrons
Onebody densities \( N=6 \), \( \hbar\omega=1.0 \) a.u.
Onebody densities \( N=6 \), \( \hbar\omega=0.1 \) a.u.
Onebody densities \( N=30 \), \( \hbar\omega=1.0 \) a.u.
Onebody densities \( N=30 \), \( \hbar\omega=0.1 \) a.u.
Or using Deep Learning Neural Networks
Replacing the Jastrow factor with Neural Networks
Conclusions and where do we stand
More material
More in depth notebooks and lecture notes are at
Making a professional Monte Carlo code for quantum mechanical simulations
http://compphysics.github.io/ComputationalPhysics2/doc/LectureNotes/_build/html/vmcdmc.html
From Variational Monte Carlo to Boltzmann Machines
http://compphysics.github.io/ComputationalPhysics2/doc/LectureNotes/_build/html/boltzmannmachines.html
Nuclear Talent course on Machine Learning in Nuclear Experiment and Theory, June 22 - July 3, 2020
Machine Learning course
Feel free to try them out and please don't hesitate to ask if something is unclear.
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