Award Abstract #0426354 Collaborative Research: ITR-(ASE+EVS)-(dmc+sim) Data Driven Simulation of the Subsurface: Optimization and Uncertainty Estimation
Rutgers University New Brunswick
3 RUTGERS PLAZA
NEW BRUNSWICK, NJ 08901 732/932-0150
NSF Program(s):
ITR FOR NATIONAL PRIORITIES
Field Application(s):
0000912 Computer Science
Program Reference Code(s):
HPCC, 9218
Program Element Code(s):
7314
ABSTRACT
Intellectual Merit. Remote sensing is employed in science and engineering problems to infer material properties when these properties can not be directly sampled. To better understand and manage our environment for safety and economic reasons, much progress has been made in imaging the subsurface and estimating physical properties based on remote sensing data. Repeated observations over targets for environmental remediation and reservoir production has become a recognized diagnostic tool for assisting management decisions. In addition, improved optimization techniques capable of responding to large, multi-resolution, disparate, dynamic datasets in a fault tolerant and adaptive fashion are a fundamental requirement for effectively estimating and minimizing the uncertainty in any data driven application. The integrated and e_ective treatment of these issues motivates the present project. The assembled research team proposes to advance the mathematical, engineering and computational foundations necessary to enhance our understanding and extend the predictive capabilities of the physical processes that govern the subsurface phenomena at multiple temporal and spatial scales. Target applications include management of aquifers for water resources, optimizing oil and gas production, and monitoring environmental risks e.g., at waste containment sites or arising from natural hazards.
The intellectual merits of the project include: (1) development of the next generation of accurate, multi-scale, coupled chemical, uid, geomechanical, and geophysical simulators for modeling instrumented subsurface environments; (2) large scale optimization techniques (based on a hybridization of global and local approaches) to drive reliable decision-making and a dynamic symbiotic feedback between computation and data; (3) deployment of an autonomic Grid middleware for providing the adequate processing substrate and data management services for (1) and (2). The realization of the above contributions will result in the Data Driven Subsurface Simulation Framework (DDSSF).
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
A. Quiroz and M. Parashar. "A Framework for Distributed Content-based Web-Services Notification in Grid Systems," Future Generation Computer Systems (FGCS) ? The International Journal of Grid Computing: Theory, Methods and Applications, v.24, 2008, p. 452.
A. Quiroz, N. Gnanasambandam, M. Parashar, and N. Sharma. "Robust Clustering Analysis for the Management of Self-Monitoring Distributed Systems," Cluster Computing: The Journal of Networks, Software Tools, and Applications, 2008.
Bhat, V; Parashar, M; Liu, H; Kandasamy, N; Khandekar, M; Klasky, S; Abdelwahed, S. "A self-managing wide-area data streaming service," CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, v.10, 2007, p. 365-383.
C. Schmidt and M. Parashar. "Squid: Enabling Search in DHT-based Systems," Journal of Parallel and Distributed Computing, v.68/7, 2008, p. 962.
Chandra, S; Li, XL; Saif, T; Parashar, M. "Enabling scalable parallel implementations of structured adaptive mesh refinement applications," JOURNAL OF SUPERCOMPUTING, v.39, 2007, p. 177-203.
G. Zhang and M. Parashar. "SESAME: Scalable, Environment Sensitive Access Management Engine
," Cluster Computing: The Journal of Networks, Software Tools, and Applications, v.9, 2006, p. 19.
G. Zhang and M. Parashar. "Corporative Defense against DDoS Attacks," Journal of Research and Practice in Information Technology (JRPIT), v.38, 2006, p. 66.
H. Liu and M. Parashar. "Rule-Based Monitoring and Steering of Distributed Scientific Applications," International Journal of High Performance Computing and Networking (IJHPCN), Inderscience Publishers, v.3, 2005, p. 272.
H. Liu and M. Parashar. "Accord: A Programming Framework for Autonomic Applications," IEEE Transactions on Systems, Man and Cybernetics, Special Issue on Engineering Autonomic Systems, IEEE Press, v.36, 2006, p. 341.
H. Liu, L. Jiang, M. Parashar and D. Silver. "Rule-Based Visualization in the Discover Computational Steering Collaboratory," FGCS ? The International Journal of Grid Computing: Theory, Methods and Applications (FGCS), v.21, 2005, p. 53.
Jiang, NY; Quiroz, A; Schmidt, C; Parashar, M. "Meteor: a middleware infrastructure for content-based decoupled interactions in pervasive grid environments," CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, v.20, 2008, p. 1455-1484.
L. Zhang and M. Parashar. "Seine: A Dynamic Geometry-based Shared Space Interaction Framework for Parallel Scientific Applications," Concurrency and Computation: Practice and Experience, John Wiley and Sons, v.18, 2006, p. 1951.
Li, Z; Parashar, M. "Enabling dynamic composition and coordination for autonomic grid applications using the Rudder agent framework," KNOWLEDGE ENGINEERING REVIEW, v.21, 2006, p. 221-230.
M. Parashar and C. Lee. "Grid Computing ? An Evolving Vision," Proceedings of the IEEE, Special Issue on Grid Computing, IEEE Press, v.93, 2005, p. 479.
M. Parashar and J.C. Browne. "Conceptual and Implementation Models for the Grid," Proceedings of the IEEE, Special Issue on Grid Computing, IEEE Press, v.93, 2005, p. 653.
M. Parashar, H. Klie, U. Catalyurek, T. Kurc, V. Matossian, J. Saltz and M Wheeler. "Application of Grid-Enabled Technologies for Solving Optimization Problems in Data-Driven Reservoir Studies," FGCS ? The International Journal of Grid Computing: Theory, Methods and Applications (FGCS), v.21, 2005, p. 19.
M. Parashar, H. Liu, Z. Li, V. Matossian, C. Schmidt, G. Zhang and S. Hariri. "AutoMate: Enabling Autonomic Grid Applications," Cluster Computing: The Journal of Networks, Software Tools, and Applications, v.9, 2006, p. 161.
M. Parashar, R. Muralidhar, W. Lee, D. Arnold, J. Dongarra and M. Wheeler. "Enabling Interactive and Collaborative Oil Reservoir Simulations on the Grid," Concurrency and Computation: Practice and Experience, John Wiley and Sons, v.17, 2005, p. 1387.
R. Sterritt, M. Parashar, H. Tianfield and R. Unland
. "A Concise Introduction to Autonomic Computing," Journal of Advanced Engineering Informatics, Engineering Applications of Artificial Intelligence, v.19, 2005, p. 181.
S. Chandra and M. Parashar. "Towards Autonomic Application-Sensitive Partitioning for SAMR Applications," Journal of Parallel and Distributed Computing, v.65, 2005, p. 519.
S. Chandra, M. Parashar, J. Yang, Y. Zhang, and S. Hariri. "Investigating Autonomic Runtime Management Strategies for SAMR Applications," International Journal of Parallel Programming,, v.33, 2005, p. 247.
S. Chandra, X. Li, T. Saif and M. Parashar. "Enabling Scalable Parallel Implementations of Structured Adaptive Mesh Refinement Applications," Journal of Supercomputing, Kluwer Academic Publishers, 2007.
S. Hariri, B. Khargharia, H. Chen, J. Yang, Y. Zhang, M. Parashar and H. Liu. "The Autonomic Computing Paradigm," Cluster Computing: The Journal of Networks, Software Tools, and Applications, v.9, 2006, p. 5.
S. Klasky, M. Beck, V. Bhat, E. Feibush, B. Ludascher, M. Parashar, A. Shoshani, D. Silver and M. Vouk. "Data management on the fusion computational pipeline," Journal of Physics: Conference Series, IOP Publishers, v.16, 2005, p. 510.
T. Kurc, U. Catalyurek, X. Zhang, J. Saltz, M. Peszynska, R. Martino, M. Wheeler, A. Sussman, C. Hansen, M. Sen, R. Seifoullaev, P. Stoffa, C. Torres-Verdin, and M. Parashar. "A Simulation and Data Analysis System for Large Scale, Data-Driven Oil Reservoir Simulation Studies," Concurrency and Computation: Practice and Experience, v.17, 1441, p. 2005.
V. Matossian, V. Bhat, M. Parashar, M. Peszynska, M. Sen, P. Stoffa and M. F. Wheeler. "Autonomic Oil Reservoir Optimization on the Grid," Concurrency and Computation: Practice and Experience, John Wiley and Sons, v.17, 2005, p. 1.
W. Bangerth, H. Klie, V. Matossian, M. Parashar, M.F. Wheeler. "An Autonomic Reservoir Framework for the Stochastic Optimization of Well Placement," Cluster Computing: The Journal of Networks, Software Tools, and Applications, v.8, 2005, p. 255.
W. Chen, C. Schmidt, M. Parashar, M. Reiss, D. Foran. "Decentralized Data Sharing of Tissue Microarrays for Investigative Research in Oncology
," Cancer Informatics, Libertas Academica, v.2, 2006, p. 373.
X. Li and M. Parashar. "Hybrid Runtime Management of Space-Time Heterogeneity for Dynamic SAMR Applications," IEEE Transactions on Parallel and Distributed Systems, v.18, 2007.
X. Li and M. Parashar. "Hybrid Runtime Management of Space-Time Heterogeneity for Dynamic SAMR Applications," IEEE Transactions on Parallel and Distributed Systems, IEEE Computer Society Press, v.18, 2007.
Z. Li and M. Parashar. "A Decentralized Computational Infrastructure for Grid based Parallel Asynchronous Iterative Applications," Journal of Grid Computing, Special Issue on Global and Peer-to-Peer Computing, Springer-Verlag, 2006, p. 1.
Z. Li and M. Parashar. "Rudder: An Agent-based Infrastructure for Autonomic Composition of Grid Applications," Multiagent and Grid System ? An International Journal, IOS Press, v.1, 2005, p. 183.
Z. Li and M. Parashar. "Enabling Dynamic Composition and Coordination of Autonomic Applications using the Rudder Agent Framework," The Knowledge Engineering Review, Cambridge University Press, v.21, 2006, p. 221.
