The Center for Data Innovation recently spoke with Juha Riippi, CEO of Quanscient, a Finland-based startup developing an AI-powered engineering-simulation platform that combines cloud and quantum computing. Riippi explained how the company’s technology helps engineers simulate complex physical systems, explore more design options, and accelerate product development across industries ranging from semiconductors to aerospace. David Kertai: What does Quanscient offer? Juha Riippi: Engineering teams today face a fundamental bottleneck. They work with increasingly complex physical systems, yet many design tools still rely on computing power and software architectures that cannot efficiently handle today’s most demanding engineering problems. As a result, companies in semiconductors, energy, automotive, aerospace, and other advanced industries struggle to evaluate large design spaces, test many design options, and understand how different variables affect performance. This slows research and development, increases reliance on physical prototypes, and limits innovation. Quanscient addresses this challenge with a cloud-based engineering platform that allows engineers to design, simulate, and optimize complex systems before building physical prototypes. The platform combines multiphysics simulation—which models several physical processes, such as electricity, heat, mechanics, and fluid flow at the same time—with high-performance computing and machine learning. Engineers can run thousands of simulations in parallel, generate large physics-based datasets, and build AI models that predict performance much faster than running a full simulation every time. Kertai: How does your platform simulate complex physical systems? Juha Riippi: Engineers begin by creating a virtual model of the product or system they want to design. They import the geometry from computer-aided design software, assign material properties, and define operating conditions such as electrical currents, temperatures, mechanical forces, or fluid flow. The platform then solves the underlying physics equations to predict how the system will perform under real-world conditions. Instead of evaluating a single design, engineers can vary nearly every design parameter