Probabalistic Machine Learning for Inverse Design of Aerodynamic Systems (Pro-ML IDeAS)

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Niskayuna, New York
Project Term:
03/24/2020 - 05/31/2022

Technology Description:

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.

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:


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. Daniel Cunningham
Project Contact:
Dr. Sayan Ghosh
Press and General Inquiries Email:
Project Contact Email:


University of Notre Dame

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