scholarly journals Constrained adaptive sampling for domain reduction in surrogate model generation: Applications to hydrogen production

AIChE Journal ◽  
2021 ◽  
Author(s):  
Julian Straus ◽  
Jabir Ali Ouassou ◽  
Brage Rugstad Knudsen ◽  
Rahul Anantharaman
Keyword(s):  
Hydrogen Production ◽  
Surrogate Model ◽  
Adaptive Sampling ◽  
Model Generation ◽  
Domain Reduction

scholarly journals An Ensemble of Adaptive Surrogate Models Based on Local Error Expectations

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Huanwei Xu ◽  
Xin Zhang ◽  
Hao Li ◽  
Ge Xiang
Keyword(s):  
Surrogate Model ◽  
Adaptive Sampling ◽  
Sampling Methods ◽  
Sampling Strategy ◽  
Surrogate Models ◽  
Local Error ◽  
Test Functions ◽  
Application Problem ◽  
Benchmark Test Functions ◽  
Global And Local

An ensemble of surrogate models with high robustness and accuracy can effectively avoid the difficult choice of surrogate model. However, most of the existing ensembles of surrogate models are constructed with static sampling methods. In this paper, we propose an ensemble of adaptive surrogate models by applying adaptive sampling strategy based on expected local errors. In the proposed method, local error expectations of the surrogate models are calculated. Then according to local error expectations, the new sample points are added within the dominating radius of the samples. Constructed by the RBF and Kriging models, the ensemble of adaptive surrogate models is proposed by combining the adaptive sampling strategy. The benchmark test functions and an application problem that deals with driving arm base of palletizing robot show that the proposed method can effectively improve the global and local prediction accuracy of the surrogate model.

scholarly journals Surrogate model generation using self-optimizing variables

2018 ◽  
Vol 119 ◽  
pp. 143-151 ◽  
Author(s):  
Julian Straus ◽  
Sigurd Skogestad
Keyword(s):  
Surrogate Model ◽  
Model Generation

Quantifying thermal variability in subduction zones via data-driven reduced-order modelling

2021 ◽  
Author(s):  
Dave May ◽  
Philip England
Keyword(s):  
Steady State ◽  
Subduction Zone ◽  
Surrogate Model ◽  
Adaptive Sampling ◽  
Subduction Zones ◽  
Thermal Structure ◽  
Data Driven ◽  
Error Estimator ◽  
Reduced Order ◽  
Dehydration Reactions

<p>Subduction zones can give rise to severe natural hazards, e.g. earthquakes, tsunami & volcanism. Improved hazard assessment may be realised through physics based modelling. The thermal structure of a subducting plate has a first order control on many aspects of the subduction zone, including: dehydration reactions; intermediate depth seismicity; melt production; formation of arc volcanoes. Subduction zones exhibit a wide variability with respect to slab age, velocity, dip, rheology and mechanical behaviour of the overriding plate. For many subduction zones the assumption of a thermo-mechanical steady-state is reasonable, hence forward models often assume the form of a kinematically driven slab causing traction-driven mantle wedge flow. Even for this simplified forward model, our understanding of how the parameters and their uncertainties influence the thermal structure is incomplete. </p><p>To address this uncertainty, here we use a data-driven model reduction technique, specifically the interpolated Proper Orthogonal Decomposition (iPOD), to define a fast-to-evaluate and surrogate model of a steady-state subduction zone that is valid over a high-dimensional parameter space. The accuracy of the iPOD surrogate model is controlled using a hyper-rectangle tree-based adaptive sampling strategy combined with a non-intrusive error estimator. To illustrate the applicability of the iPOD, we present examples in which reduced-order models are constructed for combinations of parameters related to the kinematics, rheology and geometry of the subduction zone. The examples will characterize the efficiency and accuracy of the iPOD reduced-order model when using parameter spaces that vary in dimension from 1 to 7.</p>

Sign in / Sign up

Export Citation Format

Share Document