Projects

ACUMEN

cam.ornl.gov/acumen

ACUMEN will develop scalable mathematical research that will impact neutron science and experimental facilities at ORNL.

The goal of this project is to develop innovative methods focused on core mathematical challenges that face neutron scientists and strengthen the existing integration of instrument scientists at both the Spallation Neutron Source (SNS) and High Flux Isotope Reactor (HFIR) with mathematicians at ORNL. This unified team will combine its solutions into a common framework for users at SNS and HFIR that will address:

  1. Scalable methods for inverse problems;
  2. High-dimensional approximation, Bayesian inference, and model averaging;
  3. Feature extraction and optimal subspace identification; and
  4. Data registration for neutron science.

Equinox

equinox.ornl.gov

The goal of this project is to establish a modern mathematical and statistical foundation that will enable next-generation, complex, stochastic predictive simulations. Such a foundation is critical to realizing the potential of future computing platforms, including exascale, and will ultimately enable scientists to address a fundamental question, namely "how do the uncertainties ubiquitous in all modeling efforts affect our predictions and understanding of complex phenomena?" Our collaborative approach to uncertainty quantification (UQ) combines novel paradigms in applied mathematics, statistics and computational science into a unified framework, which we call an Environment for Quantifying Uncertainty: Integrated aNd Optimized at the eXtreme-scale (EQUINOX).

Tasmanian

tasmanian.ornl.gov

The Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN is a robust library for high dimensional integration and interpolation as well as parameter calibration. The code consists of several modules that can be used individually or conjointly. The project is sponsored by Oak Ridge National Laboratory Directed Research and Development as well as the Department of Energy Office for Advanced Scientific Computing Research.


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