Learning Enabled Network Synthesis (LENS)

Default ARPA-E Project Image

East Hartford, Connecticut
Project Term:
06/08/2020 - 12/07/2022

Technology Description:

The United Technologies Research Center (UTRC) will develop an AI-accelerated search technique, LENS, to quickly discover new design concepts for energy applications. The project will combine the strengths of the two pillars of AI—logical inference and statistical learning—to achieve this task by using constraint programming, generative models, reduced order models, active learning, and rule discovery. The end goal is to accelerate the design of power converters, which have a significant impact on energy savings. UTRC’s project will aim to address key challenges in power converter design by identifying the most suitable circuit topologies and simultaneously optimizing the design of power-converter components. LENS will enable exploration of very large design spaces of circuit topologies and components by addressing the limitations of the conventional process for design of non-linear, high switching speed, and multi-dimensional power converters.

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. David Tew
Project Contact:
Dr. Kunal Srivastava
Press and General Inquiries Email:
Project Contact Email:


University of Maryland

Related Projects

Release Date: