Search research articles

Search research articles

Related Experiment Videos

Numerical methods for stochastic differential equations.

Joshua Wilkie¹

¹Department of Chemistry, Simon Fraser University, Burnaby, British Columbia, Canada V5A 1S6.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|August 25, 2004

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Numerical method for integrodifferential generalized Langevin and master equations.

Physical review. E, Statistical, nonlinear, and soft matter physics·2003

Same author

Variational principle for stochastic wave and density equations.

Physical review. E, Statistical, nonlinear, and soft matter physics·2003

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

This study introduces high-order numerical methods to accurately and efficiently solve stochastic differential equations (SDEs), overcoming limitations of existing techniques for physics applications.

Area of Science:

Physics
Computational Mathematics

Background:

Stochastic differential equations (SDEs) are crucial in physics.
Current numerical methods for SDEs suffer from low accuracy and poor stability.

Purpose of the Study:

To develop a general strategy for accurate and efficient SDE solution schemes.
To create high-order numerical methods for integrating SDEs with strong solutions.

Main Methods:

Development of novel high-order numerical integration schemes.
Application of these schemes to SDEs with known exact solutions.

Main Results:

The developed methods provide accurate and stable solutions for SDEs.
Demonstrated accuracy through error computation against known exact solutions.

Related Experiment Videos

Conclusions:

The new high-order methods offer a significant improvement over existing techniques for solving SDEs.
These methods enhance the reliability of numerical simulations in physics.