US fusion firm Commonwealth Fusion Systems (CFS) has announced partnerships with Nvidia and Siemens to develop a digital twin of its SPARC fusion reactor. The companies claim they will apply artificial intelligence (AI) and industrial data tools to accelerate the path toward commercial fusion energy. The announcement was made at CES, where CFS CEO and co-founder Bob Mumgaard appeared on stage alongside executives from Nvidia and Siemens. The SPARC tokamak reactor is a compact fusion device, expected to serve as a precursor to the company’s first commercial reactor. It is located at the company’s headquarters in Massachusetts. According to CFS, the digital twin system will integrate engineering, manufacturing, and operational data from Siemens’ Xcelerator portfolio, including NX Designcenter and Teamcenter product lifecycle management (PLM) software, already utilized by CFS to design, catalog, and manage SPARC’s complex assemblies. The data will then be fed into CFS’ modeling and simulation workflows, enabling the application of AI-powered tools across the lifecycle of the machine. In addition, CFS will utilize Nvidia Omniverse libraries and OpenUSD to combine the data with classical physics simulations and AI-enabled models, creating a virtual replica of the SPARC fusion reactor that runs alongside the physical machine. According to the company, the digital twin will allow engineers to run simulations, test hypotheses, and rapidly compare experimental results from SPARC with predicted performance. “This technology allows us to compress years of manual experimentation into weeks of understanding how these machines work,” Mumgaard said. “Through this collaboration, we’re demonstrating how AI and integrated digital engineering can accelerate progress from design to grid power.”

Full report : Commonwealth Fusion Systems teams up with Nvidia and Siemens to build an AI digital twin of its SPARC fusion reactor, accelerating its push to commercial fusion.

For more see the OODA Company Profile on Commonwealth Fusion Systems.