In the News
2018 Editors' Choice article:
An Atomistic Fingerprint Algorithm for Learning Ab Initio Molecular Force Fields. https://doi.org/10.1063/1.5008630
How sickled red blood cells stick to blood vessels
Analysis of blood from patients with sickle-cell disease reveals how cell clumping begins.
Scientists have developed a new system to classify the shapes of red blood cells by using a computational approach known as deep learning. This unique approach can potentially help doctors treat their patients with sickle cell disease. The findings were published in PLOS Computational Biology. (Read more.) Visual Credit: Xu et al.
A new book is published by George Karniadakis and Zhongqiang Zhang.
Professor George Karniadakis has coauthored a new book with Zhongqiang Zhang entitled, "Numerical Methods for Stochastic Partial Differential Equations with White Noise."
Simulations developmented by George Karniadakis and his research group provide new details of how sickle cell disease manifests inside red blood cells, which could provide help in developing new treatments. (Read News from Brown.)
George Karniadakis' research group including Lu Lu, He Li, Xin Bian and Xuejin Li have published research which reveals a new understanding of the intracellular polymerization of sickle hemoglobin (Hbs). (Read more.)