Data Assimilation: The Ensemble Kalman Filter
Data Assimilation: The Ensemble Kalman Filter
Design and Implementation of Network Performance Aware Applications Using SAGA and Cactus
E-SCIENCE '07 Proceedings of the Third IEEE International Conference on e-Science and Grid Computing
A sensor and computation grid enabled engineering model for drilling vibration research
Proceedings of the 15th ACM Mardi Gras conference: From lightweight mash-ups to lambda grids: Understanding the spectrum of distributed computing requirements, applications, tools, infrastructures, interoperability, and the incremental adoption of key capabilities
GMAC '09 Proceedings of the 6th international conference industry session on Grids meets autonomic computing
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In this paper we lay out the computational challenges involved in effectively simulating complex phenomena such as sequestering CO2 in oil and gas reservoirs. The challenges arise at multiple levels: (i) the computational complexity of simulating the fundamental processes; (ii) the resource requirements of the computationally demanding simulations; (iii) the need for integrating real-time data (intensive) and computationally intensive simulations; (iv) and the need to implement all of these in a robust, scalable and extensible approach. We will outline the architecture and implementation of the solution we develop in response to these requirements, and discuss results to validate claims that our solution scales to effectively solve desired problem sizes and thus provides the capability to generate novel scientific insight.