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Artificial intelligence and machine learning in physics
Contents
What is this talk about?
Thanks to many
One of the first many-body workshops at the ECT$*$, Trento summer 1997
Pairing in dense matter
AI/ML and some statements you may have heard (and what do they mean?)
Types of machine learning
Main categories
The plethora of machine learning algorithms/methods
What are the basic ingredients?
Low-level machine lerning, the family of ordinary least squares methods
Setting up the equations
The objective/cost/loss function
Training solution
Ridge and LASSO Regression
From OLS to Ridge and Lasso
Lasso regression
Lots of room for creativity
Selected references
Machine learning. A simple perspective on the interface between ML and Physics
ML in Nuclear Physics (or any field in physics)
Machine learning in physics (my bias): Why?
Scientific Machine Learning
ML for detectors
Physics driven Machine Learning
And more
Argon-46 by Solli et al., NIMA 1010, 165461 (2021)
Many-body physics, Quantum Monte Carlo and deep learning
Quantum Monte Carlo Motivation
Quantum Monte Carlo Motivation
Energy derivatives
Derivatives of the local energy
Why Feed Forward Neural Networks (FFNN)?
Illustration of a single perceptron model and an FFNN
Monte Carlo methods and Neural Networks
Deep learning neural networks, "Variational Monte Carlo calculations of
A\le 4
nuclei with an artificial neural-network correlator ansatz by Adams et al.":"https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.127.022502"
"Dilute neutron star matter from neural-network quantum states by Fore et al, Physical Review Research 5, 033062 (2023)":"https://journals.aps.org/prresearch/pdf/10.1103/PhysRevResearch.5.033062" at density
\rho=0.04
fm$^{-3}$
Pairing and Spin-singlet and triplet two-body distribution functions at
\rho=0.01
fm$^{-3}$
Pairing and Spin-singlet and triplet two-body distribution functions at
\rho=0.04
fm$^{-3}$
Pairing and Spin-singlet and triplet two-body distribution functions at
\rho=0.08
fm$^{-3}$
The electron gas in three dimensions with
N=14
electrons (Wigner-Seitz radius
r_s=2
a.u.), "Gabriel Pescia, Jane Kim et al. arXiv.2305.07240,":"https://doi.org/10.48550/arXiv.2305.07240"
"Efficient solutions of fermionic systems using artificial neural networks, Nordhagen et al, Frontiers in Physics 11, 2023":"https://doi.org/10.3389/fphy.2023.1061580"
Why Boltzmann machines?
The structure of the RBM network
The network
Goals
Joint distribution
Quantum dots and Boltzmann machines, 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.
Extrapolations and model interpretability
Physics based statistical learning and data analysis
Bayes' Theorem
"Quantified limits of the nuclear landscape":"https://journals.aps.org/prc/abstract/10.1103/PhysRevC.101.044307"
Observations (or conclusions if you prefer)
More observations
Possible start to raise awareness about ML in your own field
Education
Possible courses
Important Issues to think of
Observations
Future Needs/Problems
Quantified limits of the nuclear landscape
Predictions made with eleven global mass model and Bayesian model averaging
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