Director:
Carol Woodward, LLNL
Deputy Director:
Todd Munson, ANL
Director Emeritus:
Esmond G. Ng, LBNL
Team Members by Technical Area
Italics: Postdoctoral Fellow or Student
Structured Mesh
Area Lead:
Ann Almgren (LBNL) works on computational algorithms for solving PDEs and the development and implementation of new multiphysics adaptive mesh codes that are designed for the latest architectures.
Members:
Hans Johansen (LBNL) develops higher-order methods for a wide variety of PDEs, and specializes in finite volume embedded boundary approaches to complex geometries, implemented with fast solvers on GPUs.
Daniel Martin (LBNL) develops algorithms and software for solving systems of PDEs in a wide range of applications, with a particular focus on adaptive mesh refinement (AMR) and high (4th) order finite-volume methods.
Andrew Nonaka (LBNL) is interested in HPC implementations of multiphysics and multiscale algorithms for PDEs using structured adaptive mesh, particle/mesh and machine learning algorithms.
Nate Overton-Katz (LBNL) focuses on the development of cut-cell methods, adaptive mesh refinement, and high-performance computing for PDE modeling.
Unstructured Mesh
Area Lead:
Mark Shephard
RPI
Mark Shephard (RPI) focsues on technologies including automatic mesh generation of CAD geometry, automated and adaptive analysis methods, and parallel adaptive simulation technologies.
Members:
Area Lead:
Carol Woodward (LLNL) is interested in time integration and nonlinear iterative methods for solution of nonlinear PDES and in implementation and deployment of numerical software designed for high performance systems.
Members:
Cody Balos (LLNL) is a computational scientist and research software engineer with expertise in time integration methods, efficient high-performance computing, and scientific software development.
David Gardner (LLNL) is a computational scientist and SUNDIALS library developer with expertise in time integration and nonlinear solver methods for multiscale, multiphysics applications on HPC systems.
Daniel Reynolds (SMU) is an applied mathematician with expertise in time integration methods for multi-physics models. He is a lead developer for the SUNDIALS library, and was the primary developer of its ARKODE solver.
Steven Roberts (LLNL) is an applied mathematician with expertise in time integration methods for systems with multiple time scales or model fidelities, and he is a developer for the SUNDIALS library.
Chris Vogl (LLNL) is a research scientist working on the development, analysis, and implementation of numerical methods for PDEs, with a particular focus on multiscale, multiphysics systems.
Solution of Linear and Nonlinear Systems of Equations
Area Lead:
Members:
Peter McCorquodale (LBNL) works on high-performance algorithms for fast Fourier transforms and for solving PDEs on block-structured grids, and their implementations on the latest architectures.
Solution of Eigenvalue Problems
Area Lead:
Members:
Numerical Optimization
Area Lead:
Members:
Uncertainty Quantification
Area Lead:
Members:
Bert Debusschere develops methods and software for enabling predictive simulation in science and engineering, aiming to use all forms of information for more confidence in answering questions in complex systems. (SNL)
Gianluca Geracidevelops algorithms for uncertainty quantification and scientific machine learning with a particular emphasis on multi-fidelity methodologies. (SNL)
Data Analytics
Area Lead:
Members:
Ahmed Attia (ANL) develops scalable computational algorithms and software tools for large-scale inverse problems, uncertainty quantification, and optimal experimental design, with applications to energy problems.
Emil Constantinescu (ANL) works on the foundations of AI/ML for scientific computing and UQ for inverse problems.
Viktor Reshniak (ORNL) is a computational mathematician specializing in machine learning algorithms, data compression, and scientific data analysis.
Miroslav Stoyanov (ORNL) works on surrogate modeling, uncertainty quantification, high-dimensional approximation, and supercomputing.
Hoang Tran (ORNL) is an applied mathematician specializing in high-dimensional approximation, scientific machine learning and data analysis.