[Amath-seminars] Feb. 4 - Special AMATH Seminar - Benjamin Peherstorfer (MIT)

Ulrich Hetmaniuk hetmaniu at uw.edu
Wed Feb 3 13:19:21 PST 2016


Dear All,

Here is a kind reminder of Benjamin Peherstorfer's upcoming talk

Speaker: Benjamin Peherstorfer, MIT
Time: Thursday February 4th, 16h00
Location: SMI 102

Title: Data-driven multi-fidelity methods for uncertainty quantification


On Sun, Jan 31, 2016 at 3:19 PM, Ulrich Hetmaniuk <hetmaniu at uw.edu> wrote:


> Dear All,

>

> We hope you can join us for our special AMATH seminar on **Thursday**

> February 4th.

>

> Speaker: Benjamin Peherstorfer, MIT

> Time: February 4th, 16h00

> Location: SMI 102

>

> Title: Data-driven multi-fidelity methods for uncertainty quantification

>

> Abstract

> -----------

> Quantifying uncertainty in systems described by partial differential

> equations (PDEs) typically requires solving the PDEs many times for

> different realizations of the stochastic parameters. Solving the PDEs for

> thousands or even millions of parameter realizations is often intractable.

> A common remedy is to replace the high-fidelity model (system of equations)

> stemming from the discretization of the PDEs with a reduced model that

> provides low-cost approximations of the PDE solutions; however, reduced

> models induce an error into the overall result that typically leads to the

> loss of accuracy guarantees for the statistics of interest (often the error

> cannot even be quantified). We introduce multi-fidelity methods that

> combine, instead of replace, the high-fidelity model with reduced

> models---to increase the accuracy of the overall result and to ultimately

> establish accuracy guarantees. We first present an online adaptive model

> reduction approach that uses sparse data of the high-fidelity model to

> adapt a reduced model while it is evaluated. Numerical results demonstrate

> that our online adaptive reduced models achieve higher accuracies in

> approximating nonlinear PDEs than static models. In the second part, we

> introduce the multi-fidelity Monte Carlo method that combines the

> high-fidelity model with reduced models and data-fit models to efficiently

> estimate statistics of outputs of the high-fidelity model. Our

> multi-fidelity method is guaranteed to provide unbiased estimates

> ("accuracy guarantees"). In our numerical experiments with linear and

> nonlinear models from aerospace engineering, the multi-fidelity Monte Carlo

> method achieves speedups by orders of magnitude compared to methods that

> invoke a single high-fidelity or reduced model only.

>

>

> Speaker webpage

> ------------------------

> http://web.mit.edu/pehersto/www/

>

>

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman13.u.washington.edu/pipermail/amath-seminars/attachments/20160203/d5d997f4/attachment.html>


More information about the Amath-seminars mailing list