Development of an adaptive response surface method for optimization of computation-intensive models

Steenackers, Gunther; Presezniak, Flavio; Guillaume, Patrick

doi:10.1016/j.cie.2009.02.016

Cited by 33 publications

(35 citation statements)

References 18 publications

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“…The method proposed by Wang involves the domain reduction of each design variable, which its time consuming given the min imization process for both upper and lower limit. Steenackers [8] proposed a revised method that does not involve the reduction of the design space, but rather a strategy called "pan and zoo m" (for more details refer to [8]). Co mpared to Wang's method, the pan and zoom method showed a reduced time per iteration as well as the total number of iterations required to converge to the min imu m. For further examp les using adaptive method, refer to [9] - [14].…”

Section: Response Surface Methods Reviewmentioning

confidence: 99%

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Pareto Set Computation in Convex Multi-objective Design using Adaptive Response Surface Method (ARSM)

Panesso

Cruz

Bohórquez

et al. 2016

MATEC Web of Conferences

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Abstract. Optimal design problems normally involve high dimensional design spaces and multiple objective functions. Depending on the complexity of the model, the time required to explore the design space could become excessive. T his paper describes the calculation of the Pareto-optimal set based on adaptive surface methodology (ARSM) in order to reduce sim ulation times given a finite element analysis (FEA) sim ulation model. T he Paretooptimal strategy consists in the solution of a set of different single-objective problems. Each of this points is found via ARSM. T he implementation of ARSM aims to use a few initial simulation points to approximate accurately the set of single-objective functions required. T he methodology reduces significantly the number of points required to compute the efficient set compared to other strategies (e.g the exhaustive method), proving to reduce the simulation time of a computationally intensive model.

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Section: Response Surface Methods Reviewmentioning

confidence: 99%

“…Particularly, the ARSM method is based on the "Pan and Zoom" method [8]. It consists in the following steps:…”

Section: Methodology -Arsmmentioning

confidence: 99%

Pareto Set Computation in Convex Multi-objective Design using Adaptive Response Surface Method (ARSM)

Panesso

Cruz

Bohórquez

et al. 2016

MATEC Web of Conferences

View full text Add to dashboard Cite

show abstract

“…The approach followed to obtain an effective sampling strategy differs from the adaptive sampling schemes as used by for example Steenackers et al (2009), Allaix and Carbone (2011) and Dawson and Hall (2006). In those schemes the sampling density function h(x) is iteratively adapted by assigning larger sampling densities to events that contribute most to the flood risk in earlier iterations.…”

Section: Importance Samplingmentioning

confidence: 99%

An advanced method for flood risk analysis in river deltas, applied to societal flood fatality risk in the Netherlands

Bruijn

Diermanse

Beckers

2014

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper discusses a new method for flood risk assessment in river deltas. Flood risk analysis of river deltas is complex, because both storm surges and river discharges may cause flooding and the effect of upstream breaches on downstream water levels and flood risk must be taken into account. This paper presents a Monte Carlo-based flood risk analysis framework for policy making, which considers both storm surges and river flood waves and includes effects from hydrodynamic interaction on flood risk. It was applied to analyse societal flood fatality risk in the Rhine-Meuse delta.

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“…Steenackers et al 2009;Allaix and Carbone 2011). Dawson and Hal (2006) applied such an approach in a flood risk analysis of a combined coastal/fluvial system in Towyn, North Wales.…”

Section: Criteria For Selecting Importance Sampling Strategiesmentioning

confidence: 99%

Importance sampling for efficient modelling of hydraulic loads in the Rhine–Meuse delta

Diermanse

Bruijn

Beckers

et al. 2014

Stoch Environ Res Risk Assess

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During flood events, breaching of flood defences along a river system can have a significant reducing effect on downstream water levels and flood risks. This paper presents a Monte Carlo based flood risk framework for policy decision making, which takes this retention effect into account. The framework is developed to estimate societal flood risk in terms of potential numbers of fatalities and associated probabilities. It is tested on the Rhine-Meuse delta system in the Netherlands, where floods can be caused by high flows in the Rhine and Meuse rivers and/or high sea water levels in the North Sea. Importance sampling is applied in the Monte Carlo procedure to increase computational efficiency of the flood risk computations. This paper focuses on the development and testing of efficient importance sampling strategies for the framework. The development of an efficient importance sampling strategy for river deltas is more challenging than for non-tidal rivers where only discharges are relevant, because the relative influence of river discharge and sea water level on flood levels differs from location to location. As a consequence, sampling methods that are efficient and accurate for one location may be inefficient for other locations or, worse, may introduce errors in computed design water levels. Nevertheless, in the case study described in this paper the required simulation time was reduced by a factor 100 after the introduction of an efficient importance sampling method in the Monte Carlo framework, while at the same time the accuracy of the Monte Carlo estimates were improved.

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Development of an adaptive response surface method for optimization of computation-intensive models

Cited by 33 publications

References 18 publications

Pareto Set Computation in Convex Multi-objective Design using Adaptive Response Surface Method (ARSM)

Pareto Set Computation in Convex Multi-objective Design using Adaptive Response Surface Method (ARSM)

An advanced method for flood risk analysis in river deltas, applied to societal flood fatality risk in the Netherlands

Importance sampling for efficient modelling of hydraulic loads in the Rhine–Meuse delta

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