Lecture

Stability of Euler Methods: Examples

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In course

MATH-250: Advanced numerical analysis I

Construction and analysis of numerical methods for the solution of problems from linear algebra, integration, approximation, and differentiation. ...

Description

This lecture covers the stability analysis of progressive and retrograde Euler methods, including the stability conditions and examples of Cauchy problems. It also discusses the application of Euler methods to generic model problems and the trajectories close to zero. The instructor demonstrates the stability criteria through various examples and emphasizes the importance of stability in numerical simulations.

Instructors (3)

Simone Deparis

Expertise Simone Deparis is a professor at EPFL specializing in applied mathematics, numerical analysis, and the didactics of university-level mathematics. His research spans computational modeling for biomedical applications and innovations in mathematics education, with a focus on equity, self-efficacy, and assessment reform in large STEM cohorts. Mission Our group conducts research at the intersection of applied mathematics and learning sciences. In applied mathematics, we focus on modeling and simulatio

Daniel Kressner

Annalisa Buffa

Teaching & PhD PhD Students Gonzalo Bonilla Moreno, Philipp Christoph Weder, Yannis Dirk Voet, Fabio Marcinno, Ferhat Sindy, Francesco Sala, Markus Renoldner, Mohamed Ben Abdelouahab, Davor Dobrota Past EPFL PhD Students Luca Coradello, Ondine Chanon, Riccardo Puppi Courses Numerical approximation of PDEs MATH-451 The course is about the derivation, theoretical analysis and implementation of the finite element method for the numerical approximation of partial differential equations in on

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Related concepts (31)

Euler method

In mathematics and computational science, the Euler method (also called the forward Euler method) is a first-order numerical procedure for solving ordinary differential equations (ODEs) with a given initial value. It is the most basic explicit method for numerical integration of ordinary differential equations and is the simplest Runge–Kutta method. The Euler method is named after Leonhard Euler, who first proposed it in his book Institutionum calculi integralis (published 1768–1870).

Edo

Edo (), also romanized as Jedo, Yedo or Yeddo, is the former name of Tokyo. Edo, formerly a jōkamachi (castle town) centered on Edo Castle located in Musashi Province, became the de facto capital of Japan from 1603 as the seat of the Tokugawa shogunate. Edo grew to become one of the largest cities in the world under the Tokugawa. After the Meiji Restoration in 1868 the Meiji government renamed Edo as Tokyo (, "Eastern Capital") and relocated the Emperor from the historic capital of Kyoto to the city.

Stability theory

In mathematics, stability theory addresses the stability of solutions of differential equations and of trajectories of dynamical systems under small perturbations of initial conditions. The heat equation, for example, is a stable partial differential equation because small perturbations of initial data lead to small variations in temperature at a later time as a result of the maximum principle. In partial differential equations one may measure the distances between functions using Lp norms or the sup norm, while in differential geometry one may measure the distance between spaces using the Gromov–Hausdorff distance.

Edo period

The Edo period or Tokugawa period is the period between 1603 and 1867 in the history of Japan, when Japan was under the rule of the Tokugawa shogunate and the country's 300 regional daimyo. Emerging from the chaos of the Sengoku period, the Edo period was characterized by economic growth, strict social order, isolationist foreign policies, a stable population, perpetual peace, and popular enjoyment of arts and culture, colloquially referred to as Oedo.

Heun's method

In mathematics and computational science, Heun's method may refer to the improved or modified Euler's method (that is, the explicit trapezoidal rule), or a similar two-stage Runge–Kutta method. It is named after Karl Heun and is a numerical procedure for solving ordinary differential equations (ODEs) with a given initial value. Both variants can be seen as extensions of the Euler method into two-stage second-order Runge–Kutta methods.

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