Unsupervised learning methods for molecular simulation data
Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …
amounts of data produced by atomistic and molecular simulations, in material science, solid …
Machine learning and the physical sciences
Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …
for a vast array of data processing tasks, which has entered most scientific disciplines in …
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
Surrogate modeling and uncertainty quantification tasks for PDE systems are most often
considered as supervised learning problems where input and output data pairs are used for …
considered as supervised learning problems where input and output data pairs are used for …
Exploring QCD matter in extreme conditions with Machine Learning
In recent years, machine learning has emerged as a powerful computational tool and novel
problem-solving perspective for physics, offering new avenues for studying strongly …
problem-solving perspective for physics, offering new avenues for studying strongly …
A high-bias, low-variance introduction to machine learning for physicists
Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …
research and application. The purpose of this review is to provide an introduction to the core …
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
Sampling from known probability distributions is a ubiquitous task in computational science,
underlying calculations in domains from linguistics to biology and physics. Generative …
underlying calculations in domains from linguistics to biology and physics. Generative …
Machine learning for quantum matter
J Carrasquilla - Advances in Physics: X, 2020 - Taylor & Francis
Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …
Equivariant flow-based sampling for lattice gauge theory
We define a class of machine-learned flow-based sampling algorithms for lattice gauge
theories that are gauge invariant by construction. We demonstrate the application of this …
theories that are gauge invariant by construction. We demonstrate the application of this …
Equivariant flows: exact likelihood generative learning for symmetric densities
Normalizing flows are exact-likelihood generative neural networks which approximately
transform samples from a simple prior distribution to samples of the probability distribution of …
transform samples from a simple prior distribution to samples of the probability distribution of …
Sampling using gauge equivariant flows
We develop a flow-based sampling algorithm for SU (N) lattice gauge theories that is gauge
invariant by construction. Our key contribution is constructing a class of flows on an SU (N) …
invariant by construction. Our key contribution is constructing a class of flows on an SU (N) …