The DIFFERENTIATE program seeks to enhance the pace of energy innovation by incorporating machine learning into energy technology development processes. By doing so, this program aims to enhance the productivity of energy engineers in helping them to develop next-generation energy technologies.
The DIFFERENTIATE 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
- To 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.
In order to facilitate the achievement of the above-mentioned objective, ARPA-E is issuing this FOA to encourage teams consisting of mathematicians, operations research analysts, computer scientists, energy engineers, and others with applicable skills and experience to jointly work on developing the tools required to enhance the creativity and efficiency (i.e. productivity) of the energy technology design process.