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A Comparison of Adaptive Refinement Schemes for Numerical PDE
Solvers
David Lenz
UCSD
Abstract:
PDEs are often solved numerically by making a guess for the solution and then continually modifying that guess so as to better approximate the true solution. This process of modification usually involves changing the function space from which the approximation is drawn. There are many ways that the approximation space could be changed to best reduce error, and one may even wish to utilize more than one in the same problem. In this talk I will describe three strategies for refining approximation spaces and compare their performance on problems of different types.
Tuesday, February 28, 2017
11:00AM AP&M 2402
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Randolph E. Bank
Philip E. Gill
Michael Holst
Administrative Contact:
[ Click to Send Email ]
David Lenz
UCSD
Abstract:
PDEs are often solved numerically by making a guess for the solution and then continually modifying that guess so as to better approximate the true solution. This process of modification usually involves changing the function space from which the approximation is drawn. There are many ways that the approximation space could be changed to best reduce error, and one may even wish to utilize more than one in the same problem. In this talk I will describe three strategies for refining approximation spaces and compare their performance on problems of different types.
Tuesday, February 28, 2017
11:00AM AP&M 2402