| Project Detail |
The DIFFERENTIATE program seeks to leverage the emerging artificial intelligence (AI) revolution to help resolve the energy and environmental challenges of our time. The program aims to speed energy innovation by incorporating machine learning (ML) into the energy technology development process. A core part of AI, ML is the study of computer algorithms that improve automatically through experience. This approach is expected to facilitate a rapid transition to lower-carbon-footprint energy sources and systems. To organize the proposed efforts, the program uses a simplified engineering design process framework to conceptualize several ML tools that could help engineers execute and solve these problems in a manner that dramatically accelerates the pace of energy innovation. Project Innovation + Advantages: Stanford University will develop a machine-learning enhanced framework for the design of optical communications components that will enable them to operate at their physical performance limits. Information processing and communications systems use a significant fraction of total global energy. Data centers alone consume more than 70 billion kilowatt-hours per year. Much of this energy usage is intrinsic to electronic wiring. However, optical-based technologies offer a promising option to reduce energy consumption. Stanford’s design platform is intended to enable optical technologies to serve in the next generation of information processing hardware with ultra-low energy footprints. The proposed framework will use generative neural networks for global optimization of nanophotonic components, machine learning to accelerate the solving of electromagnetic field calculations, and advanced optimization concepts to calculate the upper limits in photonic device performance. Potential Impact: DIFFERENTIATE aims to enhance the productivity of energy engineers in helping them to develop next-generation energy technologies. If successful, DIFFERENTIATE will yield the following benefits in ARPA-E mission areas: |
