The push for sustainable, carbon-free energy has placed nuclear power back under the spotlight, yet the design of advanced reactors remains a massive computational hurdle.

Enter Dean Price, a professor at the Massachusetts Institute of Technology, who is integrating AI into the heart of nuclear engineering. Traditional reactor design requires solving complex nonlinear differential equations—mathematical problems that map how neutrons and thermal energy interact within a core—often requiring massive supercomputing power to solve accurately.

Price’s research targets a specific bottleneck: multiphysics modeling. By feeding vast datasets into machine-learning models, researchers can identify patterns that predict how a reactor will behave—such as temperature distribution or fission rates—without needing to solve every single equation from scratch. This approach significantly reduces the computational burden, potentially accelerating the development of small modular reactors that are safer and more flexible than current models.

Rather than replacing rigorous safety procedures, Price views these AI tools as a way to augment design processes and fill knowledge gaps. This synthesis of data science and traditional nuclear physics represents a promising new frontier in energy, turning the complex mechanics of atomic power into a faster, more accessible engineering challenge.

The push for sustainable, carbon-free energy has placed nuclear power back under the spotlight, yet the design of advanced reactors remains a massive computational hurdle.

Enter Dean Price, a professor at the Massachusetts Institute of Technology, who is integrating AI into the heart of nuclear engineering. Traditional reactor design requires solving complex nonlinear differential equations—mathematical problems that map how neutrons and thermal energy interact within a core—often requiring massive supercomputing power to solve accurately.

Price’s research targets a specific bottleneck: multiphysics modeling. By feeding vast datasets into machine-learning models, researchers can identify patterns that predict how a reactor will behave—such as temperature distribution or fission rates—without needing to solve every single equation from scratch. This approach significantly reduces the computational burden, potentially accelerating the development of small modular reactors that are safer and more flexible than current models.

Rather than replacing rigorous safety procedures, Price views these AI tools as a way to augment design processes and fill knowledge gaps. This synthesis of data science and traditional nuclear physics represents a promising new frontier in energy, turning the complex mechanics of atomic power into a faster, more accessible engineering challenge.