
We propose to develop a stochastic optimal control framework for quantifying and reducing uncertainties in deep learning by exploiting the connection between probabilistic network architectures and…

Project Summary: We propose to develop a scalable blackbox training framework for scientific machine learning (SciML) models that are nontrainable with existing automatic differentiationbased…

The Sensei project is led by Wes Bethel from Lawrence Berkeley National Laboratory and involves participants from multiple laboratories and industries. This project takes aim at a set of…

The PROTEAS project is a strategic response to the continuous changes in architectures and hardware that are defining the landscape for emerging ECP systems. PROTEAS is a flexible programming…

Parallel Aggregate Persistent Storage
Papyrus is a programming system that provides features for scalable, aggregate, persistent memory. Papyrus provides a portable and scalable…

The FASTMath SciDAC Institute develops and deploys scalable mathematical algorithms and software tools for reliable simulation of complex physical phenomena and collaborates with application…

In 1989, the U.S. Department of Energy (DOE) established the Atmospheric Radiation Measurement (ARM) user facility. From its home within DOE’s Office of Biological and Environmental Research, ARM…

The Toolkit for Adaptive Stochastic Modeling and NonIntrusive ApproximatioN is a robust library for high dimensional integration and interpolation as well as parameter calibration. The code consists…

The goal of this project is to establish a modern mathematical and statistical foundation that will enable nextgeneration, complex, stochastic predictive simulations. Such a foundation is critical…

The objective of this research is to design and evaluate a new distributed data storage paradigm that unifies the traditionally distinct application views of memory and filebased data storage into…

US Department of Energy (DOE) leadership computing facilities are in the process of deploying extremescale highperformance computing (HPC) systems with the longrange goal of building exascale…

Developing predictive tools to understand the behavior of plasmafacing components in fusion reactors. CSMD contributions include HPC implementation, uncertainty quantification, and data analysis and…

Extremescale, highperformance computing (HPC) significantly advances discovery in fundamental scientific processes by enabling multiscale simulations that range from the very small, on quantum and…

The goal of this project is to develop a highfidelity whole device model (WDM) of magnetically confined fusion plasmas, which is urgently needed to understand and predict the performance of ITER and…

2016