Locally adaptive greedy approximations for anisotropic parameter reduced basis spaces

Details Locally adaptive greedy approximations for anisotropic parameter reduced basis spaces Locally adaptive greedy approximations for anisotropic parameter reduced basis spaces

2012-04-17

arxiv.org/abs/1204.3846v1

Mathematics

Numerical Analysis

Submitted to SIAM Journal on Scientific Computing

Scientific paper

Reduced order models, in particular the reduced basis method, rely on empirically built and problem dependent basis functions that are constructed during an off-line stage. In the on-line stage, the precomputed problem dependent solution space can then be used in order to reduce the size of the computational problem. For complex problems, the number of basis functions required to guarantee a certain error tolerance can become too large in order to benefit computationally from the model reduction. To overcome this, the present work introduces a framework where local approximation spaces (in parameter space) are used to define the reduced order approximation in order to have explicit control over the on-line cost. This approach also adapts the local approximation spaces to local anisotropic behavior in the parameter space. We present the algorithm and present numerous numerical tests.

Affiliated with

Also associated with

No associations

Rating

Locally adaptive greedy approximations for anisotropic parameter reduced basis spaces does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Locally adaptive greedy approximations for anisotropic parameter reduced basis spaces, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Locally adaptive greedy approximations for anisotropic parameter reduced basis spaces will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-290444