Applying AI to scientific problems such as weather forecasting and aerodynamics is an active research area, promising to help speed up scientific discovery and improved engineering design. Typically, these applications involve modeling complex spatiotemporal processes governed by partial differential equations (PDEs) defined on continuous domains and at multiple scales—essentially learning mappings between infinite-dimensional function spaces. Traditional deep learning methods, however, map between finite-dimensional vector spaces. Neural operators overcome this limitation by generalizing deep learning to learn mappings directly between function spaces, enabling them to effectively replace traditional PDE solvers while offering substantial speed improvements—often several orders of magnitude faster. In this talk, I will introduce the fundamental concepts behind neural operators, illustrate their effectiveness on practical problems such as weather forecasting, and briefly discuss how computational efficiency can be further enhanced using tensor algebraic methods. Finally, I will touch on practical implementation aspects in Python, demonstrating how these concepts can be applied in practice using open-source software.