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Design Intelligence for Formidable Energy Reduction Engendering Numerous Totally Impactful Advanced Technology Enhancements
Program Status: 
Program Description: 

In the 250 years since the dawn of the Industrial Revolution, the pace of technology-driven economic growth has dwarfed that achieved in prior centuries. The emerging artificial intelligence revolution has similar transformational potential, which we seek to leverage to help resolve the energy and environmental challenges that are tied to the modern industrial age. 

Artificial intelligence (A.I.) makes it possible for machines to learn from experience, adjust to new inputs and perform like humans. Machine learning is a core part of A.I., and it is the study of computer algorithms that improve automatically through experience. Incorporating machine learning into the energy technology and/or product design processes is anticipated to facilitate a rapid transition to lower-carbon-footprint energy sources and systems.

The DIFFERENTIATE program seeks to enhance the pace of energy innovation by incorporating machine learning into the energy technology development process. In order to organize the proposed efforts, the program adopts and utilizes a simplified engineering design process framework to identify six general mathematical optimization problems that are common to many engineering design processes. It then conceptualizes several machine learning tools that could help engineers to execute and solve these problems in a manner that dramatically accelerates the pace of energy innovation.

Potential Impact: 

DIFFERENTIATE aims to enhance the productivity of energy engineers in helping them to develop next-generation energy technologies. The program seeks to develop machine learning tools that:

  • Enhance the creativity of the hypothesis generation (i.e., conceptual design) process by helping engineers develop new concepts and by enabling the consideration of a larger and more diverse set of design options during the hypothesis generation phase;
  • Enhance the efficiency of the high-fidelity evaluation (i.e., detailed design) process by accelerating the high-fidelity analysis and optimization of the hypothesized solution, and
  • Ultimately reduce (ideally eliminate) design iteration by developing the capability to execute “inverse design” processes in which the product design is effectively expressed as an explicit function of the problem statement.

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.

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Program Director Name: 
Dr. David Tew
Release Date: