Applied Mathematics
Developing novel mathematical methods and efficient computing algorithms to solve critical problems in science and engineering.
Applied mathematics is the application of mathematical approaches and techniques to specialized fields – like physics, biology, computer science, engineering, etc.— to solve realworld problems. Researchers use mathematical methods to model and simulate physical phenomena like fluid flows, electromagnetic devices, materials, future energy, and quantum materials, analyze experimental data, and develop new machine learning techniques.
At Berkeley Lab, our mathematics work is collaborative. Our applied mathematicians and computer scientists partner with scientists around the nation to design and develop new mathematical models and algorithms for solving scientific and engineering problems of interest to the Department of Energy, particularly those related to energy and the environment.
Discretizations & Methods

Interface Dynamics 
Numerical Linear Algebra

Math for Data 
Mathematical Software 
We develop novel numerical methods and techniques for solving mathematical problems faster, more accurately, and more efficiently. We create popular opensource software for efficient simulations, machine learning strategies, and mathematical approaches for experimental investigations. And we advance scientific discovery through collaboration.
Our experts are also highly skilled in analyzing and solving nonlinear partial differential equations, ordinary differential equations, and stochastic processes. Our numerical linear algebra specialists develop efficient linear and eigensolver algorithms and fast, scalable library implementations. Most of our algorithms have scalable implementations that target current and nextgeneration massively parallel computer architectures.