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In this paper, we have presented a strategy for studying a large observational dataset at different resolutions to obtain a sparse representation in a computationally efficient manner. Such representations are crucial for many applications from modeling and inference to visualization. Resolution here stems from the variation of the correlation strength among the different observation instances. The motivation behind the approach is to make a large dataset as small as possible by removing all the redundant information so that, the original data can be reconstructed with minimal losses of information.Our past work borrowed ideas from multilevel simulations to extract a sparse representaiton. Here, we introduce the use of multi-resolution kernels. We have tested our approach on a carefully designed suite of analytical functions along with gravity and altimetry time series datasets from a section of the Greenland Icesheet. In addition to providing a good strategy for data compression, the proposed approach also finds application in efficient sampling procedures and error filtering in the datasets. The results, presented in the article clearly establish the promising nature of the approach along with prospects of its application in different fields of data analytics in the scientific computing and related domains. [ABSTRACT FROM AUTHOR]

Abstract: Modeling and Simulation of Multiphysics Multiscale Systems (SMMS) poses a grand challenge to computational science. To adequately simulate numerous intertwined processes characterized by different spatial and temporal scales spanning many orders of magnitude, sophisticated models and advanced computational techniques are required. The aim of the SMMS workshop is to encourage and review the progress in this multidisciplinary research field. This short paper describes the scope of the workshop and gives pointers to the papers reflecting the latest developments in the field. [ABSTRACT FROM AUTHOR]

Modelling and simulation of multiscale systems constitutes a grand challenge in computational science, and is widely applied in fields ranging from the physical sciences and engineering to the life sciences and the socio-economic domain. To adequately simulate numerous intertwined processes characterized by different spatial and temporal scales (often spanning many orders of magnitude), sophisticated models and advanced computational techniques are required. Additionally, these multiscale models frequently need large scale computing capabilities as well as dedicated software and services that enable the exploitation of existing and evolving computational ecosystems. The aim of the annual Workshop on Multiscale Modelling and Simulation is to facilitate the progress in this multidisciplinary research field http://www.computationalscience.nl/MMS/ . In this paper, we reflect on the 12 years of workshop history and glimpse at the latest developments presented in 2015 in Iceland, the Land of Fire and Ice. In Section 6, we invite new workshop co-organizers. [ABSTRACT FROM AUTHOR]

This paper presents a systematic survey of open source multiphysics frameworks in the en- gineering domains. These domains share many commonalities despite the diverse application areas. A thorough search for the available frameworks with both academic and industrial ori- gins has revealed numerous candidates. Considering key characteristics such as project size, maturity and visibility, we selected Elmer, OpenFOAM and Salome for a detailed analysis. All the public documentation for these tools has been manually collected and inspected. Based on the analysis, we built a feature model for multiphysics in engineering, which captures the commonalities and variability in the domain. We in turn validated the resulting model via two other tools; Kratos by manual inspection, and OOFEM by means of expert validation by domain experts. [ABSTRACT FROM AUTHOR]