Panagiota Tsompanopoulou

On a data-driven environment for multiphysics applications

The combination of the recent advances in computational and distributed sensor network technologies provide a unique opportunity for focused efforts on high confidence modelling and simulation of multiphysics systems. Responding to this opportunity, we present in this paper the architecture of a data-driven environment for multiphysics applications (DDEMA) as a multidisciplinary problem solving environment (MPSE). The goal of this environment is to support the automated identification and efficient prediction of the behavioral response of multiphysics continuous interacting systems.The design takes into consideration heterogeneous and distributed information technologies, coupled multiphysics sciences, and sensor originating data to drive and to steer adaptive modelling and simulation of the underlying systemic behavior. The design objectives and proposed software architecture are described in the context of two multidisciplinary applications related to material structure design of supersonic platforms and fire/material/environment interaction monitoring, assessment and management. These applications of DDEMA will be distributed over a highly heterogeneous networks that extend from light and ubiquitous resources (thin portable devices/clients) to heavy GRID-based computational infrastructure.

A Platform for Delivering Multimedia Presentations on Cultural Heritage

In this paper we present a platform for delivering multimedia presentations on cultural heritage. The platform aims to enhance cultural knowledge discovery by increasing access to museums’ digital content. The platform generates rich media presentations considering the personal profile of the audience as well as its interests. The presentations may include text, images, video and sound and can be delivered via network. They can be attended either inside the museum or even outside of it e.g. in schools during a preparation class prior to a museum visit. The platform supports creation and editing of slides and presentations, updating existing presentations and projecting them, considering different roles and access levels for archeologists, tourist guides, educators and individuals.

DDEMA: a data driven environment for multiphysics applications

In this paper we present the design of a multidisciplined problem solving environment (MPSE) for supporting an efficient prediction capability for the response of multiscale interdisciplinary continuous interacting systems. This design takes into consideration information technologies, coupled multiphysics sciences, and data-driveness to steer adaptive modelling and simulation of the underlying systemic behavior. The paper describes the design objectives and software architecture of DDEMA in the context of two multidisciplinary applications related to material/structure design of supersonic platforms and fire/material/environment interaction monitoring, assessment and management.

Agent-Based Simulation of Data-Driven Fire Propagation Dynamics

Real world problems such as fire propagation prediction, can often be considered as a compositional combination of multiple, simple but coupled subproblems corresponding to analytical and computational behavior models of the systems involved in multiple domains of action. The existence of various computational resources (legacy codes, middleware, libraries, etc.) that solve and simulate the subproblems successfully, the coupling methodologies, the increasing and distributed computer power (GRID etc.) and the polymorphism and plurality of the available technologies for distributed mobile computing, such as Agent Platforms, motivated the implementation of multidisciplinary problem solving environments (MPSE) to overcome the difficulties of their integration and utilization. In this paper we present the onset of the development of computational infrastructure for the simulation of fire propagation in multiple domains using agent platforms as an informal validation of our data-driven environment for multi-physics applications (DDEMA).

Design Architecture of a Data Driven Environment for Multiphysics Applications

The design architecture of a multidisciplinary problem-solving environment (MPSE) for supporting an efficient prediction capability for the response of continuous interacting systems under multiphysics conditions is presented. The system will be referred to as a Data Driven Environment for Multiphysics Applications (DDEMA) and will be primarily differentiated from previous MPSE efforts on its usage of data for improved confidence of simulation prediction. Its architecture takes into consideration information technologies, coupled multiphysics sciences, and data-driven practices to achieve steering of adaptive modeling and simulation of the underlying systemic behavior. Special emphasis is given on middleware implementation issues based on actor-agent abstractions. A description of the design objectives and architectural variations of DDEMA are also given in the context of two multidisciplinary applications related to material/structure design of supersonic platforms and fire/material/environment interaction monitoring, assessment and management. Validation of the architecture will also be attempted in terms of the same two applications.Copyright

iMuse Mobile Tour: A personalized multimedia museum guide opens to groups

In the recent years, there is a growing interest in exploiting the advances of mobile and pervasive computing to museum environments. A mobile museum guide, named iMuse Mobile Tour is presented in this paper. The guide utilizes UHF RFID technology to provide context aware information services. It comprises predefined and self-defined tours as well as interactive games to stimulate learning. A group support service is introduced, which enables group visitors to exploit guidance services on their private mobile phones. The service provides access to information retrieved by museums RFID enabled devices.

Fast transform-based preconditioners for large-scale power grid analysis on massively parallel architectures

Efficient analysis of massive on-chip power delivery networks is among the most challenging problems facing the EDA industry today. In this paper, we present a new preconditioned iterative method for fast DC and transient simulation of large-scale power grids found in contemporary nanometer-scale ICs. The emphasis is placed on the preconditioner which reduces the number of iterations by a factor of 5X for a 2.6M-node industrial design and by 72.6X for a 6.2M-node synthetic benchmark, compared with incomplete factorization preconditioners. Moreover, owing to the preconditioners special structure that allows utilizing a Fast Transform solver, the preconditioning system can be solved in a near-optimal number of operations, while it is extremely amenable to parallel computation on massively parallel architectures like graphics processing units (GPUs). Experimental results demonstrate that our method achieves a speed-up of 214.3X and 138.7X for a 2.6M-node industrial design, and a speed-up of 1610.5X and 438X for a 3.1M-node synthetic design, over state-of-the-art direct and iterative solvers respectively when GPUs are utilized. At the same time, its matrix-less formulation allows for reducing the memory footprint by up to 33% compared to the memory requirements of the best available iterative solver.

A 3-D Fast Transform-based preconditioner for large-scale power grid analysis on massively parallel architectures

Efficient analysis of on-chip power delivery networks is one of the most challenging problems that EDA is confronted with. This paper addresses the problem of simulating general multi-layer power delivery networks with significant via resistances. An iterative solution method is combined with an efficient and extremely parallel preconditioning mechanism based on the application of a 3D Fast Transform solver, which enables harnessing the computational resources of massively parallel architectures, such as GPUs. Experimental evaluation of the proposed methodology on a set of large-scale industrial benchmarks demonstrates a speed-up of 290.2X for a 2.62M-node design over a state-of-the-art parallel direct solver, and a speed-up of 75.5X for a 10.51M-node design over a parallel iterative solver with a general-purpose preconditioner, when GPUs are utilized.

Dynamic data driven methodologies for multiphysics system modeling and simulation

We are presenting a progress overview associated with our work on a data-driven environment for multiphysics applications (DDEMA). In this paper, we emphasize the dynamic-data-driven adaptive modeling and simulation aspects. Adaptive simulation examples of sensor-originating data-driven precomputed solution synthesis are given for two applications. Finally, some of the computational implementation details are presented.

Multiobjective Unfolding of Shared Power Consumption Pattern Using Genetic Algorithm for Estimating Individual Usage in Smart Cities

In smart cities residential homes are fully equipped with information networking and computing technologies and are connected to the power grid via intelligent meters. Connectivity of meters allows formation of groups of residents, which are physically close, and as a result individual consumptions can be aggregated into a shared consumption. In this paper an approach of unfolding shared consumption and making inferences about resident personal usage is presented. The proposed approach tackles the problem of unfolding as a multiobjective problem in which a set of residential profiles is fitted to the measured consumption. A solution to the multiobjective problem is sought by using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) that utilizes the Pareto optimality theory to identify an optimal solution. The approach is applied to a set electricity consumption signals for making inferences about the personal energy usage of residential participants in the shared consumption pattern.