Skip to Main Content U.S. Department of Energy
Computational Sciences & Mathematics

Research Areas

research areas Coarse GrainingFast SolversScalable Algorithms and ApplicationsStochastic MethodsConcurent CouplingContinuum Methods Particle Based Methods

  • Coarse Graining

    Mori-Zwanzig (task lead Panos Stinis) focuses on making the MZ formalism computationally manageable to open new avenues of application for coarse-graining methods.

  • Particle-Based Methods

    (task lead George Karniadakis) focuses on extending the spectrum of Lagrangian DPD-SDPD-SPH methods for modeling and making connections to the MZ formalism.

  • Continuum Methods

    (task lead Martin R. Maxey) focuses on develop grid-based high-order methods that are efficient for multi-phase dynamics and scale well at the system level while also capturing dominant fluctuations at mesoscale.

  • Stochastic Methods

    (task lead Paul Atzberger) focuses on developing methods to quantify the uncertainty in Hamiltonian systems, and calibrate the model parameters using data obtained from experimental measurements or from simulations of systems at a higher resolution.

  • Concurrent Coupling

    (task lead Pavel Bochev) focuses on developing concurrent multi-model coupling methods to address the challenge of bridging the spatial and temporal regimes that are individually described by distinct mathematical models and computational models.

  • Fast Solvers

    (task lead Jinchao Xu) focuses on developing new physics-based Schur complements and preconditions for multiscale dynamics within the algebraic multigrid.

  • Scalable Algorithms and Applications

    (task lead Wenxiao Pan) focuses on developing scalable method implementations within a software infrastructure that facilitates coupling with existing scalable simulation codes to allow method validation and verification based on simulations of real problems.

Collaboratory on Mathematics for Mesoscopic Modeling of Materials (CM4)


Seminar Series

Computing Research at PNNL

Science at PNNL

Computing Research

View All Highlights