@Article{CAM-7-6, author = {}, title = {From dxiu@purdue.edu Thu Apr 1 10:27:11 2010 Date: Wed, 31 Mar 2010 22:26:51 -0400}, journal = {CAM-Net Digest}, year = {2010}, volume = {7}, number = {6}, pages = {7--7}, abstract = {

Dear colleagues,

After many months of preparation, the "International Journal for Uncertainty Quantification" (IJ4UQ) is now launched.

http://uncertainty-quantification.com/

The journal is now open for paper submissions and we are looking forward receiving your work and with your support making this the premier Journal for Uncertainty Quantification. We plan for a rigorous review process but anticipate a rapid turnaround publication schedule.

For more information on the Editorial Board and submission/ registration process,please visit the above web site. The registration form requires a manual approval (to avoid duplicate registrations) but you can avoid that by asking us to pre-register you. For this and any other information, please do not hesitate tocontact our editorial office at uqjournal@gmail.com

Aims and Scope
--------------
The International Journal for Uncertainty Quantification disseminates information of permanent interest in the areas of analysis, modeling,design and control of complex systems in the presence of uncertainty.The journal seeks to emphasize methods that cross stochastic analysis, statistical modeling and scientific computing. Systems of interest are governed by differential equations possibly with multiscale features.

Topics of particular interest include representation of uncertainty,propagation of uncertainty across scales, resolving the curse of dimensionality, long-time integration for stochastic PDEs, data-driven approaches for constructing stochastic models, validation, verification and uncertainty quantification for predictive computational science, and visualization of uncertainty in high- dimensional spaces. Bayesian computation and machine learning techniques are also of interest for example in the context of stochastic multiscale systems, for model selection/classification, and decision making. Reports addressing the dynamic coupling of modern experiments and modeling approaches towards predictive science are particularly encouraged. Applications of uncertainty quantification in all areas of physical and biological sciences are appropriate.

Editor-in-Chief,
Nicholas Zabaras, Cornell University
Associate Editor,
Dongbin Xiu, Purdue University




}, issn = {}, doi = {https://doi.org/2010-CAM-16737}, url = {https://global-sci.com/article/78110/from-dxiu-at-purdueedu-thu-apr-1-102711-2010-date-wed-31-mar-2010-222651-0400} }