Anurag Saha Roy, CBO of Cruise, discusses the need to accelerate scientific research and development (R&D) and the role of machine learning (ML) in this process. Cruise is developing ML tools to help scientists and engineers speed up the R&D process by bridging the gap between simulations and reality. By using automatic differentiation and differentiable simulations, Cruise enables scientists to understand and improve their designs more effectively. The goal is to make Cruise the default differentiable simulation and design partner in various fields, starting with quantum technologies and expanding into other areas such as MRI devices and silicon photonics.
- The pace of scientific research and development needs to be accelerated to keep up with advancements in technologies like quantum computing, silicon photonics, and MRI devices.
- Cruise is developing machine learning tools to help scientists and engineers speed up the R&D process by bridging the gap between simulations and reality.
- By using automatic differentiation and differentiable simulations, Cruise enables scientists to understand and improve their designs more effectively.
- The goal is to make Cruise the default differentiable simulation and design partner in various fields, starting with quantum technologies and expanding into other areas such as MRI devices and silicon photonics.
00:00 Introduction and the Need for Accelerated Scientific R&D
02:45 Bridging the Gap: Simulations and Reality in Scientific R&D
07:48 Automatic Differentiation and Differentiable Simulations
10:05 Replacing Black Box Models with White Box Models
11:22 Improving Fidelity and Understanding Parameters in Quantum Computing
28:00 Cross-Pollination of Ideas and Insights in Scientific R&D
scientific research, development, machine learning, acceleration, simulations, quantum computing, differentiable simulations, automatic differentiation, scientific R&D, scientific simulation, interdisciplinary conferences, technology, supercritical
