Efficient Simulation Strategy Research Articles

On the Structural Stability of a Short Three Stranded β-sheet Peptide (Betanova): Replica Exchange Molecular Dynamics Simulation Study

On the Structural Stability of a Short Three Stranded β-sheet Peptide (Betanova): Replica Exchange Molecular Dynamics Simulation Study

A SAEM algorithm for the estimation of template and deformation parameters in medical image sequences

This paper is about object deformations observed throughout a sequence of images. We present a statistical framework in which the observed images are defined as noisy realizations of a randomly deformed template image. In this framework, we focus on the problem of the estimation of parameters related to the template and deformations. Our main motivation is the construction of estimation framework and algorithm which can be applied to short sequences of complex and highly-dimensional images. The originality of our approach lies in the representations of the template and deformations, which are defined on a common triangulated domain, adapted to the geometry of the observed images. In this way, we have joint representations of the template and deformations which are compact and parsimonious. Using such representations, we are able to drastically reduce the number of parameters in the model. Besides, we adapt to our framework the Stochastic Approximation EM algorithm combined with a Markov Chain Monte Carlo procedure which was proposed in 2004 by Kuhn and Lavielle. Our implementation of this algorithm takes advantage of some properties which are specific to our framework. More precisely, we use the Markovian properties of deformations to build an efficient simulation strategy based on a Metropolis-Hasting-Within-Gibbs sampler. Finally, we present some experiments on sequences of medical images and synthetic data.

Robust Parallel Computations through Randomization

In this paper we present an efficient general simulation strategy for computations designed for fully operational BSP machines of n ideal processors, on n-processor dynamic-fault-prone BSP machines. The fault occurrences are fail- stop and fully dynamic, i.e., they are allowed to happen on-line at any point of the computation, subject to the constraint that the total number of faulty processors may never exceed a known fraction. The computational paradigm can be exploited for robust computations over virtual parallel settings with a volatile underlying infrastructure, such as a NETWORK OF WORKSTATIONS(where workstations may be taken out of the virtual parallel machine by their owner). Our simulation strategy is Las Vegas (i.e., it may never fail, due to backtrack- ing operations to robustly stored instances of the computation, in case of locally unrecoverable situations). It adopts an adaptive balancing scheme of the workload among the currently live processors of the BSP machine. Our strategy is efficient in the sense that, compared with an optimal off-line adversarial computation under the same sequence of fault occurrences, it achieves

Increasing solution stability for finite-element modeling of elasto-plastic shell response

The present article introduces a highly efficient numerical simulation strategy for the analysis of elasto-plastic shell structures. An isoparametric Finite Element, based on a Finite Rotation Reissner–Mindlin shell theory in isoparametric formulation, is enhanced by a Layered Approach for a realistic simulation of nonlinear material behaviour. A general material model including isotropic hardening effects is embedded into each material point. A new, highly accurate integration scheme is combined with consistently linearized constitutive relations in order to achieve quadratic rate of convergence. A global Riks–Wempner–Wessels iteration scheme enhanced by a linear Line-Search procedure was used to trace arbitrary deformation paths. Numerical examples show the efficiency of the present concept.

Accurate and efficient power simulation strategy by compacting the input vector set

Accurate power estimation of digital CMOS circuit can be obtained by explicit simulation. However, power dissipation is input dependent. To obtain an accurate power estimate, a large input vector set has to be used resulting in large simulation time. One solution is to generate a representative vector set of the original input vector set that contains only a few thousand vectors which can then be simulated in a reasonable time. This paper addresses the problem of vector compaction for power simulation. We compact the input vector set such that the statistical properties which affect the power consumption are preserved. Experimental results show that compaction ratio of 100X is achieved with less than 2% average error in the power estimates.

A note on approximation techniques used for process optimization

The need for increasingly complex models for process simulation has led several researchers to develop more efficient steady-state simulation strategies. In a common approach, rigorous physical property calculations are replaced by simplified ones which approximate locally the behavior of the rigorous ones. Periodically the rigorous models are executed to check if the simplified ones are still accurate enough, and, if not, parameters for the simplified ones are adjusted so both the rigorous and simplified models again predict the same values locally. Convergence is assumed when no adjustments are needed for these parameters and the simplified model is converged. This approach works well for phase equilibrium and even general process simulation problems. Some researchers have extended this approach to optimization problems as well. In this note we discuss problems which arise when adapting this approach for process optimization problems. We also present three examples where the use of simplified models can lead to detection of false optima or convergence failures.