In the News

In the News

An atomistic fingerprint algorithm for learning ab initio molecular force fields

New Machine learning system identifies shapes of red blood cells










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."

Computer Models provide new understanding of sickle cell disease

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.)

Understanding of the interactions between sickle cell fibers

Researchers in Applied Mathematics discover ways to understand better sickle cell disease

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.)