Peter Graf's research with NREL's Computational Science Center is focused on bringing state-of-the-art applied mathematics to bear on computational problems involving renewable energy. Current research is split roughly between artificial intelligence (AI) and quantum computing.
Graf leads a team studying the applicability of high-performance computing and AI across the spectrum of activities involved with connected and autonomous vehicles perception and control. Other work in AI includes incorporating reinforcement learning control into distributed control of energy systems, including the power grid, buildings operation, transportation networks, and wind farms.
Graf also leads NREL’s efforts in quantum computing algorithms for near-term quantum hardware, involving both adapting energy-related problems to known algorithms, such as quantum approximate optimization algorithm and variational quantum eigensolver, and exploring beyond these toward the ultimate capabilities of the quantum computational model as they relate to energy applications.
Past research includes inverse material design, multi-scale simulation and optimization of organic photovoltaics, multi-scale simulation and parameter estimation of Li-ion batteries, statistical loads estimation and stochastic optimization of wind turbines, hierarchical simulation and optimization in systems biology, biomass characterization, and more.
Graf graduated Phi Beta Kappa, with distinction, from Stanford University in 1989. After years as a C/C++ programmer, he entered graduate school and in 2003 received his doctorate in mathematics from the University of California at Berkeley. His thesis involved optimization of model reduction for systems of ordinary differential equations, under advisor Alexandre Chorin.
Ph.D., Mathematics, University of California at Berkeley
B.S., Symbolic Systems (a combination of philosophy, psychology, computer Science, and linguistics), Stanford University