Paul G. Allen School of Computer Science & Engineering and Applied Mathematics courses in programming languages, scientific computing, and dynamical systems.
Complete semester courses and structured lecture series recorded at University of Washington.
Steve Brunton's intensive optimization sequence covering mathematical problem formulation, convex sets, convex functions, vector norms, Jensen's inequality, global optimality guarantees, and applications across machine learning, inverse problems, and control theory.
State-space dynamical systems, stability eigenvalues, controllability, observability, Linear Quadratic Regulators (LQR), and Kalman filtering derived for modern control engineering.
Self-contained research seminars, public lectures, and invited colloquia.
Steven L. Brunton introduces HydroGym, a novel reinforcement learning platform for controlling complex fluid dynamics, with connections to recent Nature publications.
High-density applied math lecture connecting X = UΣV* directly to scientific computing, reduced-order modeling, and high-dimensional data compression.
Explore the construction of one-dimensional Fourier quasicrystals through the lens of multivariate stable polynomials, delving into the rich interplay between algebra and harmonic analysis.
Natalie Paquette introduces a novel formula for computing form factors and scattering amplitudes in four-dimensional gauge theories, linking them to 2D chiral algebras.
Shows how lifting finite-dimensional nonlinear ODEs into infinite-dimensional observable spaces enables spectral decomposition and linear optimal control.
Professors, researchers, and invited lecturers recorded at University of Washington.
Matrix exponentials, stability eigenvalues, feedback regulators, and nonlinear locomotion
Stanford University · University of Washington · MIT
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