Modeling and Simulation

Sophia Henneberg focuses on developing, utilizing, and extending optimization tools to identify new, promising stellarator designs, which are a promising path toward fusion energy. Ethan Peterson focuses on improving radiation transport and transmutation methods for the design of fusion technologies, as well as whole-facility modeling for fusion power plants. Dean Price focuses on the simulation and control of advanced reactors, with expertise in uncertainty quantification, scientific machine learning, and artificial intelligence for nuclear applications.

PhD student Miranda Schwacke works with Prof Bilge Yildiz to explore how computing inspired by the human brain can fuel energy-efficient artificial intelligence.

Mingda Li, an Associate Professor of Nuclear Science and Engineering, has been named a 2025 fellow of the American Physical Society (APS). Nominated by the Topical Group on Data Science (GDS), Li was cited, “[f]or pioneering the integration of artificial intelligence with...

With SCIGEN, researchers can steer AI models to create materials with exotic properties for applications like quantum computing.

The collaboration has led to new fuels and a variety of other projects to enable clean, safe nuclear energy.

A new study by Mingda Li and his team finds an oft-ignored effect can be used to probe an important property of semiconductors.

The new study by Haruko Wainwright and researchers at Lawrence Berkeley National Lab and the University of Orléans suggests modeling could be used to validate the safety of nuclear disposal sites.

Fusion’s future depends on decoding plasma’s mysteries. Simulations can help keep research on track and reveal more efficient ways to generate fusion energy.