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The core of Scientific Computing includes the construction of mathematical models and quantitative analysis tools as well as the use of computers to analyze and solve scientific problems. The list of topics that appears below is by no means meant to be exhaustive. Topics of interest include, but are not limited to, the following:

    • Algorithms and Methods: Monte Carlo methods and applications; Numerical methods and simulation; Quantum computing; Computational number theory; Optimization and approximation methods; Probabilistic and randomized methodologies; Computational geometry; Computational biology; Computational chemistry; Computational fluid dynamics; Computational physics; Computational mechanics; Computational electromagnetics and computational electrodynamics; computational sociology; Splines and wavelets; Inversion problems; Cellular automata; Ordinary and partial differential equations; Stochastic differential equations; Integral equations; Finite element methods; Multi-level and Multi-grid methods; Operational research; Dynamical systems; Nonsymmetric solvers; Engineering problems and emerging applications.
    • High Performance Computing and Communication Systems: Cluster computing; Supercomputing; Cloud computing; Autonomic computing; P2P computing; Mobile computing; Grid computing; Parallel/distributed architectures and algorithms; Networks and interconnection networks; Reliability and fault-tolerance; The use of building block processors; Real-time and embedded systems; Multimedia communications, systems, and applications; Software tools and environments for Scientific Computing; Performance analysis, evaluation and monitoring; Distributed systems; FPGA, multicore, GPU, SOC and applications; Nanotechnology in HPC; High-performance mobile computation and communication; Petri Nets; Web-based simulation and computing; and Emerging technologies.
    • Modeling, Simulation and Visualization Methods: Computational modeling and simulation in science and engineering; Molecular modeling and simulation; Simulation languages and tools; Performance modeling; Information and scientific visualization; Modeling methodologies; Visual interactive simulation and modeling; Visualization tools and systems for simulation and modeling; Process, device, circuit simulation and modeling Multi-level modeling; CAD/CAE/CAM; Agent based simulation; Analytical and stochastic modeling techniques and applications; Chaos modeling, control and signal transmission; Simulation of complex systems; Simulation of intelligent systems; Vision and visualization; Prototyping and simulation; Biomedical visualization and applications; Discrete and numeric simulation; Modeling and simulation frameworks, and Virtual reality and simulation.
    • Big Data and Data Analytic: Software and hardware architectures; Big Data visualization; Services; Data analytics; toolkits; open platforms; business processes; Managing, analyzing, and using large volumes of structured and/or unstructured data; Simulation and modeling; Big Data and decision sciences and analytics; Scientific Crowdsourcing; Case studies; and Applications.



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