
Atomic nuclei are strongly interacting, quantum manybody systems displaying fascinating properties. They exhibit emergent phenomena characteristic of large complex systems while at the same…

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

In this effort, we propose to establish a modern mathematical foundation that will enable nextgeneration computational methods for polynomial approximation of highdimensional systems, having a…

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

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…

During the twoyear span of this project, we will focus on seven different problems to demonstrate the ability of ACUMEN to solve the specific mathematical challenges at experimental facilities at…

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

2016

2015