we are organizing a mini at the upcoming ECCOMAS Congress 2016 in Crete Island, Greece, 5-10 June 2016: http://www.eccomas2016.org/ on “Reduced Basis, POD and PGD Model Order Reduction Techniques”
More info about the mini (MS1101): http://www.eccomas2016.org/content/accepted-minisymposia
We would like to invite you to submit an abstract for a contributed talk in our mini-simposium, deadline is November 29, 2015.
We hope you could join and please let us know if you could make it. Feel free to extend this invitation to your collaborators, of course.
Gianluigi Rozza, SISSA, Trieste, Italy on behalf his co-organizers
Francisco Chinesta, EC Nantes, France,
Elias Cueto, U. Saragoza, Spain,
Antonio Huerta, UPC Barcelona, Spain,
Pierre Ladeveze, ENS Cachan, France
Numerous models encountered in science and engineering remain nowadays, despite the impressive progresses attained recently in computational simulation techniques, intractable when the usual and well experienced discretization techniques are applied for their numerical simulation. Thus, different challenging issues are waiting for the proposal of new alternative advanced simulation techniques, the brute force approach being no more a valuable alternative. A first challenging issue concerns the treatment of highly multidimensional models arising from quantum mechanics or kinetic theory models of solids or fluids, including micro and nano-structured complex fluids or stochastic problems with numerous variables. The main challenge in the treatment of this kind of models is related to the curse of dimensionality because when one applies standard mesh based discretization the number of degrees of freedom involved scales exponentially with the dimension of the space concerned.
Another issue concerns the solution of transient multiscale models (usually strongly non linear and coupled, and always of high size). These models arise in computational mechanics (involving a large variety of constitutive behaviors, couplings etc.). In this context, the use of standard incremental discretization techniques becomes inefficient from the computational time viewpoint. In general, these models involve different characteristic times and space scales ranging through several decades.
Moreover, in the context of problems optimization of inverse identification many direct problems must be solved. Again, alternative advanced computational techniques are urgently needed for solving parametric partial differential equations. Examples of dimension reduction are POD, RB and PGD among many other techniques. Moreover, when using any kind of reduced modeling, verification is a crucial point because we are introducing an inevitable error whose quantification is extremely important in engineering design.
In the session, the most recent advances attained by the former techniques will be pointed out and new incipient alternatives explored.