While our genomes are essentially static, our microbiomes are inherently dynamic. The microbial communities we harbor in our bodies change throughout our lives due to many …
Introduction to Functional Data Analysis provides a concise textbook introduction to the field. It explains how to analyze functional data, both at exploratory and inferential levels. It also …
We introduce a simple, rigorous, and unified framework for solving nonlinear partial differential equations (PDEs), and for solving inverse problems (IPs) involving the …
Networks and network analysis are arguably one of the largest growth areas of the early twenty-first century in the quantitative sciences. Despite roots in social network analysis …
Figure 1.1 provides a prototype for the type of data that we shall consider. It shows the heights of 10 girls measured at a set of 31 ages in the Berkeley Growth Study (Tuddenham …
M Yin, X Zheng, JD Humphrey… - Computer Methods in …, 2021 - Elsevier
We employ physics-informed neural networks (PINNs) to infer properties of biological materials using synthetic data. In particular, we successfully apply PINNs to extract the …
This book is intended to provide fundamental statistical concepts and tools relevant to the analysis of genetic data arising from population-based association studies. Elementary …
Ordinary differential equations (ODEs) are a powerful tool for modeling dynamic processes with wide applications in a variety of scientific fields. Over the last two decades, ODEs have …
The empirical performance of neural ordinary differential equations (NODEs) is significantly inferior to discrete-layer models on benchmark tasks (eg image classification). We …