Complex Systems Simulation and Optimization
NREL's computational science experts work on the design of complex data structures, software systems, algorithms, and numerical methods for efficient simulation and modeling.
In the study of systems, complexity may arise within the modeled system itself, or in the associated simulation or optimization framework. NREL's capabilities are aligned with three main enabling topic areas to embrace and manage complexity: high-performance computing (HPC), computational science, and applied mathematics.
Capabilities in complex systems simulation and optimization include:
NREL HPC experts collaborate with researchers to take full advantage of advanced computing hardware—such as Peregrine—and software resources necessary to advance clean technologies.
- Applications: Implementations and strategies for using large, highly parallel computer resources—from interacting with users on system use, to improving the mechanisms for using HPC systems.
- Parallel Performance: Improvements to computational science and engineering research by using hundreds to tens of thousands of processing cores all applied to solving a single problem.
- Software Architecture: Design and optimization of software to take advantage of large hardware resources to address research and analysis goals.
Computational scientists are domain scientists using computers as their primary method of investigating and researching scientific, engineering, and analysis problems. NREL experts in this space focus their research on the following types of systems:
- Atomic, Molecular, Nano, and Biological Systems: Systems where material, chemical, or biological properties emergent over multiple scales or unusual spaces are the primary concern.
- Clean Energy Systems: Modern electric grid and integrated energy systems.
Applied mathematicians improve NREL's ability to address research challenges by bringing new and advanced mathematical techniques to scientific, technical, and analysis problems. Capabilities in this area include:
- Interacting Systems: Mathematics required to describe, model, simulate, solve, explore, and optimize complex systems, whether those systems are interacting atoms, systems of chemical reactions, or engineered systems describing the electric grid.
- Uncertainty Quantification: Mathematics to quantify uncertainty are used to enable decision-making and numerics that take the uncertainty of variable renewable resources into account.
- Stochastic Optimization and Control: Formulation and implementation of advanced optimization and control techniques that balance fidelity of representation with computational complexity and take into account uncertainty.
Group Manager, Complex Systems Simulation and Optimiziation