Applied mathematics involves using mathematical approaches and techniques to solve real-world problems in specialized fields such as physics, biology, computer science, and engineering. Researchers apply mathematical methods to model and simulate physical phenomena—such as fluid flows, electromagnetic devices, materials, future energy, and quantum materials—analyze experimental data and develop innovative machine-learning techniques.
At Berkeley Lab, our mathematics work is highly collaborative. Our applied mathematicians and computer scientists partner with scientists nationwide to design and develop mathematical models and algorithms that address scientific and engineering challenges of interest to the Department of Energy, particularly in the areas of energy and the environment.
We create novel numerical methods to solve mathematical problems more quickly, accurately, and efficiently. Our team develops widely used open-source software for efficient simulations, machine learning strategies, and mathematical approaches for experimental investigations, advancing scientific discovery through collaboration.
Our experts excel in analyzing and solving nonlinear partial differential equations, ordinary differential equations, and stochastic processes. Additionally, our numerical linear algebra specialists develop efficient linear and eigensolver algorithms, along with scalable library implementations. Most of our algorithms are optimized for current and next-generation massively parallel computing architectures.
Our Research Pillars:
- Discretizations & Methods
- Interface Dynamics
- Numerical Linear Algebra
- Math for Data
- Mathematical Software
- Optimization
- Partial Differential Equations
- Uncertainty Quantification
