Characterizing cell–cell communication and tracking its variability over time are crucial for
understanding the coordination of biological processes mediating normal development …

Understanding how intratumoral immune populations coordinate antitumor responses after
therapy can guide treatment prioritization. We systematically analyzed an established …

Multi-fidelity modeling and learning is important in physical simulation related applications. It
can leverage both low-fidelity and high-fidelity examples for training so as to reduce the cost …

Abstract Fourier Neural Operator (FNO) is a popular operator learning framework. It not only
achieves the state-of-the-art performance in many tasks, but also is efficient in training and …

Characterizing cell-cell communication and tracking its variability over time is essential for
understanding the coordination of biological processes mediating normal development …

The brain produces diverse functions, from perceiving sounds to producing arm reaches,
through the collective activity of populations of many neurons. Determining if and how the …

Many modern time-series datasets contain large numbers of output response variables
sampled for prolonged periods of time. For example, in neuroscience, the activities of 100s …

This paper presents an efficient variational inference framework for a family of structured
Gaussian process regression network (SGPRN) models. We incorporate auxiliary inducing …

Tensors, also known as multidimensional arrays, are useful data structures in machine
learning and statistics. In recent years, Bayesian methods have emerged as a popular …

Computational physics is an interdisciplinary field that involves physics, mathematics, and
computer science. The main goal is to devise computational techniques to tackle various …