DiaMonD: An Integrated Multifaceted Approach to Mathematics at the Interfaces of Data, Models, and Decisions is a U.S. Department of Energy Mathematical Multifaceted Integrated Capabilities Center (MMICC) involving researchers from Colorado State University, Florida State University, Los Alamos National Laboratory, Massachusetts Institute of Technology, Oak Ridge National Laboratory, University of Texas at Austin, and Stanford University.

MISSION

METHODS AND ANALYSIS

Develop advanced mathematical methods and analysis for multimodel, multiphysics, multiscale model problems driven by frontier DOE applications

THEORY AND ALGORITHMS

Create theory and algorithms for integrated inversion, optimization, and uncertainty quantification for these complex problems

DISSEMINATION

Disseminate the philosophy of a data-to-decisions approach to modeling and simulation of complex problems to the broader applied math and computational science communities

Training of young researchers has been a significant focus and accomplishment of DiaMonD. To date, 21 young DiaMonD researchers have gone on to tenure-track university positions or permanent Lab position. Learn more

OVERVIEW

The DiaMonD team conducts research at the interfaces of the core applied mathematics areas, employing the cross-cutting themes, driven by DOE scientific applications, organized under research thrusts.

DRIVING SCIENTIFIC APPLICATIONS

subsurface energy and
environmental flows
(Dawson, Gable & Juanes)

materials for energy
storage and conversion
(Marzouk & Pannala)

ice sheet dynamics
and future sea level
(Ghattas & Gunzburger)

CORE APPLIED MATHEMATICS AREAS

multiphysics methods
(Estep)
multiscale methods
(Oden)
fast algorithms
(Ying)
model validation & inadequacy
(Moser)
multimodel & multifidelity methods
(Willcox)
model
reduction
(Gunzburger)
inverse problems & data fusion
(Biros)
optimal design & control
(Ghattas)
uncertainty quantification
(Marzouk)

CROSS-CUTTING THEMES

advanced discretization • adaptivity • scalability • data-model integration
adjoints & sensitivity • dimensionality reduction • stochasticity • managing uncertainty