Synergistic Discovery and Design (SD2)
Abstract
The Synergistic Discovery and Design (SD2) program will develop data-driven methods to accelerate scientific discovery and robust design in domains that lack complete models. Engineers regularly use high-fidelity simulations to create robust designs in complex domains such as aeronautics, automobiles, and integrated circuits. In contrast, robust design remains elusive in domains such as synthetic biology, neuro-computation, and polymer chemistry due to the lack of high-fidelity models. The SD2 program will develop tools to enable robust design despite the lack of complete scientific models. This will involve collecting raw experimental data into a data and analysis hub; developing computational techniques that extract scientific knowledge directly from experimental data; and creating data sharing tools and metrics that facilitate collaborative design. The program will adopt synthetic biology as the primary application domain. Alternative domains of interest include chemistry, material science, and neuro-computation. SD2 builds on techniques being developed under the Probabilistic Programming for Advancing Machine Learning program.
Document Details
- Document Type
- Accomplishment
- Publication Date
- Oct 01, 2018
- Source ID
- 04bc427da78020afa53dfa29d40d0e3b