In a time of aging infrastructure and increasingly smart control of buildings, the ability to predict how buildings use energy -- and how much energy they use -- has remained elusive, until now. Researchers from Saudi Arabia, China and the United States collaborated to develop a smarter way to predict energy use through a method that involved artificial systems, computational experiments and parallel computing.
Studies of animal movement and behavior--including those addressing disease spread and animal conservation--should monitor animals at both regular and irregular time points to improve understanding of animal movement behavior, according to a new study by Penn State statisticians. The study is the first to provide guidance about sampling regimes for this type of biological research.
This conferenceis sponsored by the SIAM Activity Group on Uncertainty Quantification (SIAG/UQ).is being held in cooperation with the GAMM Activity Group on Uncertainty Quantification (GAMM AG UQ) and the American Statistical Association (ASA).
It takes a tremendous amount of computer simulations to create a device like an MRI scanner that can image your brain by detecting electromagnetic waves propagating through tissue. The tricky part is figuring out how electromagnetic waves will react when they come in contact with the materials in the device. SMU researchers have developed an algorithm that can be used in a wide range of fields - from biology and astronomy to military applications and telecommunications - to create equipment more efficiently and accurately.
An array of fundamental and unanswered questions in mathematics lie at the intersection of geometry and topology. With a 60 million kroner grant from the Danish National Research Foundation to establish a new research center, University of Copenhagen mathematicians hope to solve a few of these decades-old problems.
A joint research group consisting of the Institute of Statistical Mathematics (ISM) and the National Institute for Materials Science (NIMS) has developed approximately 140,000 machine learning models capable of predicting 45 different types of physical properties in small molecules, polymers and inorganic materials. The joint group then made XenonPy.MDL -- a pre-trained model library -- publicly available.
Random bit sequences are key ingredients of various tasks in modern life and especially in secure communication. In a new study researchers have determined that generating true random bit sequences, classical or quantum, is an impossible mission. Based on these findings, they have demonstrated a new method of classified secure communication.
In a study published in the International Journal of Heat and Mass Transfer, Ahmad Najafi, Ph.D., a professor in Drexel's College of Engineering, and his faculty collaborator, Jason Patrick, Ph.D., from North Carolina State University, report on how a computational technique they developed can quickly produce designs for 3D printing carbon-fiber composite materials with an internal vasculature optimized for active-cooling.