# Samuel Alfred ForemanArgonne National Laboratory | ANL · Argonne Leadership Computing Facility

Samuel Alfred Foreman

PhD. Physics (2019)

Working on Machine Learning and generative models for simulations in Lattice Quantum Field Theory.

## About

14

Publications

1,428

Reads

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46

Citations

Citations since 2017

Introduction

Current research interests include the application of machine learning to MCMC/HMC simulations of lattice QCD.
Personal website:
https://www.samforeman.me

Education

August 2015 - July 2019

August 2010 - May 2015

August 2010 - May 2015

## Publications

Publications (14)

We generalize the Hamiltonian Monte Carlo algorithm with a stack of neural network layers and evaluate its ability to sample from different topologies in a two dimensional lattice gauge theory. We demonstrate that our model is able to successfully mix between modes of different topologies, significantly reducing the computational cost required to g...

Our work seeks to transform how new and emergent variants of pandemic causing viruses, specially SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSLMs) which can learn the evolutionary landscape of SARS-CoV-2 genomes. By pretraining on over 110 million pr...

Contribution from the USQCD Collaboration to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021).

There is great potential to apply machine learning in the area of numerical lattice quantum field theory, but full exploitation of that potential will require new strategies. In this white paper for the Snowmass community planning process, we discuss the unique requirements of machine learning for lattice quantum field theory research and outline w...

We propose using Normalizing Flows as a trainable kernel within the molecular dynamics update of Hamiltonian Monte Carlo (HMC). By learning (invertible) transformations that simplify our dynamics, we can outperform traditional methods at generating independent configurations. We show that, using a carefully constructed network architecture, our app...

We introduce LeapfrogLayers, an invertible neural network architecture that can be trained to efficiently sample the topology of a 2D $U(1)$ lattice gauge theory. We show an improvement in the integrated autocorrelation time of the topological charge when compared with traditional HMC, and propose methods for scaling our model to larger lattice vol...

Using the example of configurations generated with the worm algorithm for the two-dimensional Ising model, we propose renormalization group (RG) transformations, inspired by the tensor RG, that can be applied to sets of images. We relate criticality to the logarithmic divergence of the largest principal component. We discuss the changes in link occ...

Using the example of configurations generated with the worm algorithm for the two-dimensional Ising model, we propose renormalization group (RG) transformations, inspired by the tensor RG, that can be applied to sets of images. We relate criticality to the logarithmic divergence of the largest principal component. We discuss the changes in link occ...

The maximum energy density of a capacitor is comparatively small due to large leak currents that thermally degrade the system. We study a three-plate system with nanometer gaps between the plates. Two negatively charged plates (cathodes) sandwich a thin, positively charged inner plate (anode). The dynamics of the electrons, in gaps of such a capaci...

Machine learning has been a fast growing field of research in several areas dealing with large datasets. We report recent attempts to use Renormalization Group (RG) ideas in the context of machine learning. We examine coarse graining procedures for perceptron models designed to identify the digits of the MNIST data. We discuss the correspondence be...

## Projects

Projects (2)

Improve the efficiency of Hamiltonian Monte Carlo samplers for generating gauge configurations in lattice gauge theory and lattice QCD.
Project repo:
https://github.com/nftqcd/fthmc