Probabalistic Machine Learning for Inverse Design of Aerodynamic Systems (Pro-ML IDeAS)
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:
GE Global Research will develop a probabilistic inverse design machine learning (ML) framework, Pro-ML IDeAS, to take performance and requirements as input and provide engineering designs as output. Pro-ML IDeAS will calculate the design explicitly without iteration and overcome the challenges of ill-posed inverse problems. Pro-ML IDeAS will use GE’s Bayesian hybrid modeling with multi-fidelity intelligent design and analysis of computer experiments and a novel probabilistic invertible neural network (INN). The proposed framework can be applied to general complex design problems. The designs of interest are turbomachinery components, applicable to not only industrial gas turbines, but also aviation turbine engines, aero derivative engines, wind turbines, and hydro turbines.
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:
Seek U.S. technological competitive advantage by leading the development of machine-learning enhanced engineering design tools.
Use these tools to solve our most challenging energy and environmental problems by facilitating an economically attractive transition to lower carbon-footprint energy sources and systems.
Reap the economic productivity benefits associated with the commercial adoption of the resulting higher-value energy technologies and associated products.
ARPA-E Program Director:
Dr. David TewProject Contact:
Dr. Sayan Ghosh
Press and General Inquiries Email:
ARPA-E-Comms@hq.doe.govProject Contact Email:
University of Notre Dame