Computational Science & Numerical Analysis
Computational science is a key area related to physical mathematics. The problems of interest in physical mathematics often require computations for their resolution. Conversely, the development of efficient computational algorithms often requires an understanding of the basic properties of the solutions to the equations to be solved numerically. For example, the development of methods for the solution of hyperbolic equations (e.g. shock capturing methods in, say, gas-dynamics) has been characterized by a very close interaction between theoretical, computational, experimental scientists, and engineers.
Faculty
Laurent Demanet Applied analysis, Scientific Computing
Alan Edelman Parallel Computing, Numerical Linear Algebra, Random Matrices
Steven Johnson Waves, PDEs, Scientific Computing
Gilbert Strang Numerical Analysis, Partial Differential Equations
Instructors & Postdocs
Peter Baddoo Fluid Dynamics, Complex Analysis, Machine Learning
Keaton Burns PDEs, Spectral Methods, Fluid Dynamics
Pui Tung Choi Applied and Computational Geometry, Metamaterials, Quantitative Biology, Medical Imaging
Andrew Horning Numerical Analysis, Scientific Computing, Large-Scale And Infinite-Dimensional Spectral Problems
Adam Kay Hydrodynamic Quantum Analogues
Raphaël Pestourie Inverse design in nanophotonics, efficient approximate solvers in optics, deep surrogate for PDES.
Adrien Scheuer Multiscale modeling, model order reduction, scientific machine learning
Graduate Students*
Mo Chen Optimization, Scientific Computing
Sarah Greer Imaging, inversion, signal processing
Sung Woo Jeong Numerical Linear Algebra, Random Matrix Theory
George Stepaniants Statistical Learning of PDEs, Continuous Neural Networks
*Only a partial list of graduate students