Lectures related to High Order PDE-Convergence: ADI-Type Integrators

Explores fundamental thermodynamics concepts, laws, energy transfer, and system analysis.

Explores the Random Feature Method for solving PDEs using machine learning algorithms to approximate high-dimensional functions efficiently.

Explores inference for stochastic differential equations, focusing on numerical methods and convergence analysis.

Covers the stability analysis of ODEs using numerical methods and discusses stability conditions.

Explores the periodic table of elements and Assyr Abdulle's significant contributions to numerical analysis.

Covers high order methods for space discretisation in linear differential systems.

Explores sampling algorithms for constrained systems, emphasizing the work of Benedict Leimkuhler from the University of Edinburgh.

Covers explicit stabilized methods for stiff stochastic differential equations, analyzing their properties and applications.

Explores diffusion from a macroscopic perspective, emphasizing the derivation of the diffusion equation through mass conservation and fixed flux law.

Covers non-linear ordinary differential equations, including separation, Cauchy problems, and stability conditions.

Covers parametric functions, integrals, and the origin of plasticity in metals.

Explores advanced space discretization techniques in numerical analysis for solving differential systems efficiently and accurately.

Explores Computational Molecular Design, emphasizing Mathematical Theory, High Performance Computing, and In Vivo Experiments.

Covers eigenvalues and eigenvectors in linear algebra.

Covers the proof of the existence of y and the resolution of EDOs with practical examples.

Explores boundary value problems, finite difference method, and Joule heating examples in 1D.

Covers the formalism and bases of dynamic systems, including differential equations and non-linear systems.