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Page is a React SPA; body content is client-rendered and not extractable via static fetch. Meta description confirms program existence but no deadline information was retrievable. ARPA-E FOA exchange does not list DIFFERENTIATE as currently open.
Differentiate (Innovative Development in Energy-Related Applied Science for Energy Technology Development) is sponsored by ARPA-E (Advanced Research Projects Agency-Energy). The DIFFERENTIATE program aims to enhance the pace of energy innovation by incorporating machine learning into the energy technology development process, supporting early-stage applied research and proof-of-concept for transformational and disruptive changes in energy technology.
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[](https://arpa-e. energy. gov/programs-and-initiatives/view-all-programs/differentiate) A **.
gov** website belongs to an official government organization in the United States. Apply for FundingARPA-E Annual SummitDepartment of Energy [](https://arpa-e. energy.
gov/) * Agriculture & Bioenergy I invest in transformative energy technology" I create transformative energy technology" 2. Programs & Initiatives - View All Programs 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.
## Innovation & Advantages 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 three 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. 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.
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.
## DIFFERENTIATE Projects ### Lawrence Berkeley National Laboratory (LBNL) Deep Learning and Natural Language Processing for Accelerated Inverse Design of Optical Metamaterials Energy Efficient Integrated Photonic Systems based on Inverse Design Accelerating Coupled HVAC/Building Simulation with a Neural Component Architecture ### General Electric (GE) Global Research Probabalistic Machine Learning for Inverse Design of Aerodynamic Systems (Pro-ML IDeAS) MLSPICE: Machine Learning based SPICE Modeling Platform for Power Magnetics ## DIFFERENTIATE Spotlight #### Dr. Daniel Cunningham Deputy Director for Technology Publish Date: Nov 19 2019 #### Department of Energy Announces $15 Million for Development of Artificial Intelligence and Machine Learning Tools Publish Date: Apr 05 2019 #### Department of Energy Announces $20 Million to Develop Artificial Intelligence and Machine Learning Tools _Today, the U.S. Department of Energy’s (DOE’s) Advanced Research Projects Agency-Energy (ARPA-E) announced up to $20 million in funding to accelerate the incorporation of machine learning and artificial intelligence into energy technology and product design processes.
_ [](http://www. linkedin. com/company/advanced-research-projects-agency-energy-arpa-e-) [](https://www.
facebook. com/arpaegov) [](https://www. youtube.
com/user/ARPAEGOV) Advanced Research Projects Agency‑Energy ### Agency Contact Center
According to the current listing, eligibility includes: ARPA-E programs typically target universities, national laboratories, and private companies; specific eligibility details are not extractable from the client-rendered page. Confirm the full requirements in the official notice before applying.
The current listing shows up to $500,000. Verify award ceilings, matching requirements, and allowable costs in the official notice.
Differentiate (Innovative Development in Energy-Related Applied Science for Energy Technology Development) is funded by ARPA-E (Advanced Research Projects Agency-Energy). Verify program details on the funder's official page before applying.
Yes — this listing is flagged as national in scope, so applicants across the U.S. may apply, subject to the sponsor's other eligibility criteria.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
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