Statistical, Nonlinear, and Soft Matter Physics Commons^™

A Statistical Mechanics Approach To Reinforcement Learning, Jacob Adamczyk

Graduate Doctoral Dissertations

Reinforcement learning (RL), the study of optimal decision-making over long timescales in stochastic systems, has recently seen remarkable advances due in large part to the efforts of the deep learning community. RL has witnessed great success in solving problems in video games, robotics, biological control, and language modeling. However, a unified statistical mechanics framework to understand and develop the corresponding algorithms is lacking. To address this issue, we begin by showing that the reinforcement learning problem can be formulated and solved using the tools of statistical mechanics. Drawing on physical principles of free energy minimization and invariance, we address important …

Questioning Reality: The Progressive Development Of Modern Physics, Joshua Lancman

STEM Month

Humanity has a tendency to divide time. The past is distinct from the present which is entirely separate from the future. In supposedly 20-20 vision history is neatly divided into different sections, distinct eras with sharp lines between them. What is present and in the future is always modern. What is past is something else with another name.

Yet time is not divided so neatly. We know this living through it: years and decades blend into one another in a non-uniform progression. To divide human history into separate eras is a necessary simplification, as it helps to ascribe order onto …

Questioning Reality: The Progressive Development Of Modern Physics, Joshua Lancman

STEM for Success Showcase

Humanity has a tendency to divide time. The past is distinct from the present which is entirely separate from the future. In supposedly 20-20 vision history is neatly divided into different sections, distinct eras with sharp lines between them. What is present and in the future is always modern. What is past is something else with another name.

Yet time is not divided so neatly. We know this living through it: years and decades blend into one another in a non-uniform progression. To divide human history into separate eras is a necessary simplification, as it helps to ascribe order onto …

Development Of Graphical Models And Statistical Physics Motivated Approaches To Genomic Investigations, Yashwanth Lagisetty

Dissertations and Theses (Open Access)

Identifying genes involved in disease pathology has been a goal of genomic research since the early days of the field. However, as technology improves and the body of research grows, we are faced with more questions than answers. Among these is the pressing matter of our incomplete understanding of the genetic underpinnings of complex diseases. Many hypotheses offer explanations as to why direct and independent analyses of variants, as done in genome-wide association studies (GWAS), may not fully elucidate disease genetics. These range from pointing out flaws in statistical testing to invoking the complex dynamics of epigenetic processes. In the …

Predicting The Hydration Free Energy Of Small Alkanes And Alcohols From Custom, Electronic Structure-Based Force Fields, T. Ryan Rogers

Graduate Theses and Dissertations

Mathematical theories reveal the fundamental physics involved in experimentalphenomena. Computer models of such theories are routinely used to corroborate or explain experiments and predict properties of chemical systems. Therefore, an important effort in computational chemistry is the development of more accurate and efficient chemical models. Current-generation models are only beginning to approach experimental-quality predictions of hydration free energies (HFEs).Using computations of quantum mechanical (QM) forces and classical simulations based on these forces, I investigate models to predict several properties of solutes and solutions. This dissertation is a collection of projects exemplifying methods used to gain insight into chemical systems.

Simulations …

A Network Theoretical Approach To Real-World Problems: Application Of The K-Core Algorithm To Various Systems, Kate Burleson-Lesser

Dissertations, Theses, and Capstone Projects

The study of complex networks is, at its core, an exploration of the mechanisms that control the world in which we live at every scale, from particles no bigger than a grain of sand and amino acids that comprise proteins, to social networks, ecosystems, and even countries. Indeed, we find that, regardless of the physical size of the network's components, we may apply principles of complex network theory, thermodynamics, and statistical mechanics to not only better understand these specific networks, but to formulate theories which may be applied to problems on a more general level. This thesis explores several networks …