Implementation of Neural networks using PyTorch
We review and discuss the structure and implementation of basic neural networks using PyTorch. Polynomial fitting, classification, and...
Sparse Gaussian Processes
We review the motivation for and implementation of sparse Gaussian processes. Special attention is given to the variational method of...
Expectation Propagation, Model uncertainty in RANS simulation, Variational Auto-Encoders
Introduction to Expectation Propagation [Slides] Souvik Chakraborty Expectation propagation (EP) is an approximate Bayesian inference...
Nick Geneva joins with two fellowships
We welcome our new group member, Nick Geneva, graduated from University of Delaware with a BS, Honors Mechanical Engineering (with...
New Funding from Rolls Royce
We have recently received a funding from Rolls Royce on ...
Uncertainty Quantification for Density Functional Theory Calculations
How certain are we of the accuracy of DFT simulations? In this project we try to provide an answer through a framework to quantify the...
Uncertainty Quantification and Propagation for Alloys using the Cluster Expansion
What confidence can we have in alloy property calculations when we use surrogate models? In this project, we develop a Bayesian UQ...
Our new website is launched today.
Stayed tuned as we will update more about our current research in the coming weeks.
Deep Gaussian Process Post #1
Intro to DGP and everything...
