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adaptive sampling Bayesian algorithm for constrained black-box optimization problems

AICHE JOURNAL 2025年第4期71卷

作者： Fan, Shuyuan Hong, Xiaodong Liao, Zuwei Ren, Congjing Yang, Yao Wang, Jingdai Yang, Yongrong Zhejiang Univ Dept Chem & Biol Engn State Key Lab Chem Engn Hangzhou 310058 Peoples R China Zhejiang Univ Engn Res Ctr Funct Mat Intelligent Mfg Zhejiang Pr ZJU Hangzhou Global Sci & Technol Innovat Ctr Hangzhou 311215 Peoples R China Zhejiang Univ Ningbo Innovat Ctr Hangzhou Zhejiang Peoples R China

Constrained black-box optimization (CBBO) has become increasingly popular in process optimization. Algorithms often encounter difficulties in balancing feasibility and optimality, with some even failing to find feasible solutions. This article introduces an adaptive sampling Bayesian optimization algorithm (ASBO) to solve CBBO problems effectively. The developed infill sampling criterion introduces an adaptive acquisition function to facilitate multistage optimization. The three stages consist of exploring feasible solutions, balancing feasibility and optimality, and optimizing. Furthermore, a hybrid method is proposed for complex problems. A gradient-based optimizer (GBO) aids in constructing the posterior distribution, thereby enhancing the identification of feasible regions. Additionally, four auxiliary strategies are developed to enhance stability and accelerate convergence in simulation-based optimization. The effectiveness of the proposed algorithm is validated through three benchmark problems and two process optimization cases. Comparative analysis against state-of-the-art algorithms demonstrates better iteration efficiency of ASBO algorithms.

关键词： Bayesian optimization adaptive sampling constrained black-box optimization Gaussian process

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adaptive sampling with tensor leverage scores for exact low-rank third-order tensor completion

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APPLIED MATHEMATICAL MODELLING 2025年 138卷

作者： Chen, Xuan Jiang, Tai-Xiang Hu, Yexun Yu, Jinjin Ng, Michael K. Southwestern Univ Finance & Econ Sch Comp & Artificial Intelligence Chengdu Peoples R China Kash Inst Elect & Informat Ind Kashi Peoples R China Minist Educ Engn Res Ctr Intelligent Finance Chengdu Peoples R China Hong Kong Baptist Univ Dept Math Hong Kong Peoples R China

Tensor completion aims at estimating the missing entries from the incomplete observation. Under the tensor singular value decomposition framework, the exact recovery of a low-tubal-rank third- order tensor could be achieved via convex optimization with high probability if the tensor satisfies the tensor incoherence condition. In this work, we show that, when the random selection of entries is made adaptive to a distribution which is dependent on the coherence structure of the tensor, any low-tubal-rank tensor of the size n x n x n with tubal-rank r can be exactly recovered with high probability from as few as O(rn(2) log(2)(n)) randomly chosen entries. In practice, tensor leverage scores are not known a priori, and we design a two-phase adaptive sampling strategy to obtain the leverage scores. Numerical experiments on synthetic and real-world third-order tensor data sets are used to validate our theoretical results and illustrate that the tensor recovery performance of the proposed two-phase adaptive sampling scheme is better than that of the other state-of-the-art methods.

关键词： Tensor completion adaptive sampling Tensor singular value decomposition Low-rank

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adaptive sampling-based surrogate modeling for composite performance prediction

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COMPUTATIONAL MATERIALS SCIENCE 2025年 250卷

作者： Mojumder, Satyajit Ciampaglia, Alberto Washington State Univ Sch Mech & Mat Engn Pullman WA 99163 USA Politecn Torino Dept Mech & Aerosp Engn Turin Italy

Composite structures experience varying strain states across different locations during loading due to the inherent material inhomogeneities. These strain states can be accurately captured using Digital Image Correlation (DIC)-based strain maps and incorporated into composite modeling through Representative Volume Element (RVE) analysis, which is expensive. In this paper, we develop an adaptive sampling-based surrogate model approach to efficiently map the high-dimensional experimental strain-states to stress-states (obtained through limited physics-based RVE analysis) for a woven composite structure. In this approach, a Gaussian Process Regression (GPR) model is used to guide the adaptive sampling process and build the surrogate model that can predict the stress response of any composite sample within the design space. We used this trained model to predict the stress and failure of a woven composite samples with holes and compared with experiment. Our findings demonstrate that adaptive sampling reduced the number of experiments and simulations significantly while still predict to failure and stress effectively for a composite part. This combined experimental, numerical, and data-driven approach can accelerate the design process for architectural composite materials and enhance their performance.

关键词： adaptive sampling Gaussian Process Regression Surrogate model Woven composites

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adaptive sampling Technique for Free-Form Surface of Aero-Engine Blades Based on Discrete Curvature

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2025年第2期26卷 325-344页

作者： Huang, Liang Cao, Yan Peng, Runling Li, Hao Wang, Tuo Wei, Guoxin Xian Technol Univ Mechatron Engn Xian 710021 Peoples R China Xian Technol Univ Comp Sci & Engn Xian 710021 Peoples R China Xian KunLun Ind Grp Co LLC Adv Mfg Res Inst Xian 710043 Peoples R China

As the aero-engine blade surface is a typical representative of a free-form surface, its sampling and reconstruction accuracy directly determines the evaluation and correction of subsequent processing technology. In this study, based on a case study of a small engine rotor blade, the blade surface (including concave and convex variations) was divided into thick rectangular meshes based on the idea of mesh subdivision, and the rectangular meshes were refined into primary triangular meshes by Delaunay triangulation. Second, the local Gaussian curvature of the vertices of each primary refined triangle mesh was estimated by combining the Moreton-Sequin method. Then, according to the curvature change in the two connected sampling points on the edge of each primary refined triangle, the adaptive sampling point interpolation along with the curvature change on the boundary of the original triangulation triangle and the densified sampling point distribution, which satisfies the idealized triangulation requirement, were further realized. This approach happens through continuous iteration by introducing a curvature relaxation factor and a greedy algorithm. This method can be used to adaptively adjust the distribution and number of sampling points according to the change in the geometric characteristics of the free-form surface and the curvature distribution of each part. The comparison between this method and the mainstream equidistant sampling method in the blind sampling method and the recent mainstream adaptive sampling method, which uses biquartic B-spline curves, demonstrates that the method proposed in this study can accurately and efficiently represent the complex special-shaped free-form surface in blade free-form surfaces with the fewest sampling points while achieving the highest sampling efficiency and the highest model reconstruction precision.

关键词： Aeroengine blades Free-form surface adaptive sampling Discrete curvature Greedy algorithm

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Goal-oriented adaptive sampling for projection-based reduced-order models

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COMPUTERS & FLUIDS 2025年 290卷

作者： Blais, Donovan Nadarajah, Siva Biondic, Calista McGill Univ Dept Mech Engn 817 Rue Sherbrooke Ouest 270 Montreal PQ H3A 0C3 Canada

Modern aircraft design involves a large number of design parameters from a multitude of disciplines. Obtaining high-fidelity solutions for all combinations of such parameters is computationally unfeasible. Although the solution to a large-scale system of equations is generally an element of a large-dimensional space, the solution may actually lie on a reduced-order subspace induced by parameter variation. In order to capture this subspace, samples of the high-dimensional system called snapshots are used to build a reduced-order model. These models have generated interest as a means to compute high-fidelity solutions at a much lower computational cost. However, little value can be placed in a reduced-order solution without some quantification of its error. The dual-weighted residual can be used to obtain error estimates between the outputs of different models. Using dual-weighted residual error estimates in conjunction with a radial basis function interpolation, this work introduces a novel adaptive sampling method that chooses snapshots iteratively such that a prescribed output error tolerance is estimated to be met on the entirety of a parameter space. The adaptive sampling procedure is demonstrated on a one-dimensional Burgers' equation and two-dimensional inviscid flows.

关键词： Proper orthogonal decomposition (POD) Petrov-Galerkin projection Projection-based model Reduced-order model (ROM) adaptive sampling Error estimation

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Parametric Machine Learning-Based adaptive sampling Algorithm for Efficient IoT Data Collection in Environmental Monitoring

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JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2025年第1期33卷 1-22页

作者： Algabroun, Hatem Hakansson, Lars Linnaeus Univ Mech Engn Dept S-35252 Vaxjo Sweden

With the IoT trend, wireless sensors are gaining growing interest. This is due to the possibility of installing them in locations inaccessible to wired sensors. Although great success has already been achieved in this area, energy limitation remains a major obstacle for further advances. As such, it is important to optimize sampling to a sufficient rate to catch important information without excessive energy consumption. One way to achieve sufficient sampling is by using an algorithm for adaptive sampling named dynamic sampling rate algorithm (DSRA);however, this algorithm requires an expert to set and tune its parameters, which might not always be readily available. This study aims to further develop this algorithm to be machine learning based to tune these parameters. To achieve this goal, the algorithm was modified and an optimization strategy that considers a predetermined error threshold was developed. Then the algorithm was implemented using simulated and real data with a set of predetermined errors thresholds to observe its performance. The results showed that the developed algorithm exhibited adaptive sampling behavior, and it could collect data efficiently depending on the predetermined error threshold. Based on the results, it is possible to conclude that the developed algorithm endows sensors with adaptive sampling capabilities based on the signal rate of change.

关键词： Dynamic sampling rate algorithm (DSRA) adaptive sampling Simulated annealing Data reduction

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On adaptive sampling techniques for metamodels based on NURBS entities from unstructured data

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2025年 437卷

作者： Zani, M. Panettieri, E. Montemurro, M. Univ Bordeaux Bordeaux INP Arts & Metiers Inst Technol CNRSI2M UMR 5295INRA F-33405 Talence France

The paper investigates the influence of adaptive sampling strategies on the generation of a metamodel based on Non-Uniform Rational Basis Spline (NURBS) entities, obtained from unstructured data, with the purpose of improving accuracy while minimising computational resources. The metamodel is defined as solution of a constrained non-linear programming problem and it is solved through a three-step optimisation process based on a gradient-based algorithm. Moreover, this paper introduces a generalised formulation of the NURBS-based metamodel capable of handling unstructured sampling data, enabling simultaneous optimisation of control points and weights. Sensitivity analyses are performed to evaluate the influence of various adaptive sampling techniques, including cross-validation-based and geometry-based strategies, on the resulting metamodel, in terms of accuracy and computational costs. Analytical benchmarks functions and a complex real-world engineering problem (dealing with the nonlinear thermomechanical analysis of a part produced with the fused deposition modelling technology) are used to prove the effectiveness of the NURBS-based metamodel coupled with adaptive sampling strategies in achieving high accuracy and efficiency.

关键词： Metamodel Surrogate model NURBS Optimisation adaptive sampling Unstructured data Additive manufacturing

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An adaptive sampling strategy for aero-engine blade surface based on the momentum conservation principle

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第4期36卷 045017-045017页

作者： Chang, Zhanyou Luo, Jun Song, Liang Nie, Xuequan Bai, Zhisong Chongqing Univ Key Lab Optoelect Technol & Syst Minist Educ Chongqing 400044 Peoples R China

The spatial distribution and density of inspection points are critical factors influencing the accuracy and efficiency of aero-engine blade quality assessment. This study proposes an adaptive sampling methodology to optimize measurement precision and operational efficiency for blade surface inspections through dynamic adjustment of sampling point locations and quantities based on geometric feature variations. A parametric blade model is established using non-uniform rational B-splines for geometric representation, with reconstruction accuracy evaluated through median Hausdorff distance metrics. An innovative adaptive sampling strategy based on momentum conservation principle is developed, where multi-feature parameters (including curvature, torsion, chord height, and arc length) are incorporated as mass terms to characterize blade geometry. Velocity terms are introduced to dynamically balance the influence weights of different geometric features during the sampling process. Through three comparative experiments, the proposed method demonstrates superior performance compared to conventional approaches including equal arc length sampling, chordal tolerance method, modified equal chord height sampling, equal moment theory sampling, and bending moment theory sampling. Results indicate that under equivalent sampling point quantities, this methodology achieves the minimum deviation between reconstructed and actual blade surfaces.

关键词： adaptive sampling aero-engine blade momentum conservation principle NURBS reconstruction error

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An adaptive sampling framework for data-efficient modelling of marine antibiotic-resistance genes (ARGs)

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KNOWLEDGE-BASED SYSTEMS 2025年 310卷

作者： Cong, Tian Craw, Pascal Ickowicz, Adrien Francis, Neil CSIRO Mineral Resources 26 Dick Perry Ave Perth WA 6151 Australia CSIRO Environm 3 Castray Esplanade Hobart Tas 7004 Australia CSIRO Data61 3 Castray Esplanade Hobart Tas 7004 Australia

Identifying antibiotic-resistance genes (ARGs) in marine systems traditionally relies on extensive laboratory- based analysis. Data-driven modelling of ARGs is an emerging and promising approach that could enable real-time water quality monitoring, reducing both field and lab work, and associated costs. This work demonstrates a combination of Squared Prediction Error (SPE), Kernel Principal Component Analysis (KPCA) and Gaussian Processes (GP) for marine monitoring. The work advances real-time decision-making for both when to sample and data-efficient modelling by identifying when environmental conditions are likely to be novel. This was quantified by the SPE-KPCA model as a difference between current water conditions and those previously sampled. By identifying already-sampled environmental conditions, the number of water samples that were collected was reduced. The modelling of the level of ARGs is based on the GP predictive model. The method adaptively updated SPE-KPCA and GP models as new data were collected. The proposed framework was validated on both synthetic and real-life data. The validation demonstrated that the GP model, using a subset of the data sequentially selected by the SPE-KPCA model, led to similar prediction accuracy to the case of using all data, showing the potential for reducing the costs of data acquisition in this problem.

关键词： adaptive sampling Squared prediction error Kernel Principal Component Analysis Gaussian processes Data-efficient modelling

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A low power SAR ADC with fine-tuned time based adaptive sampling technique for ECG monitoring application in 180 nm CMOS

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INTEGRATION-THE VLSI JOURNAL 2025年 103卷

作者： Kandpal, Naveen Singh, Anil Agarwal, Alpana Thapar Inst Engn & Technol Elect & Commun Engn Patiala 147001 India

This work proposes an adaptive sampling SAR Analog-to-Digital converter (ADC) with a novel fine-tuned timebased sampling technique to dynamically adjust the sample rate for normal and abnormal ECG signals based on the characteristics of the incoming ECG signal. By integrating machine learning, the ADC adapts to varying signal conditions, ensuring accurate capture of essential data while maintaining energy efficiency, thereby enhancing the effectiveness of portable and wearable health monitoring devices. Unlike conventional methods, the Analog front end uses a time-based technique that effectively identifies and digitizes all critical information present in ECG signals. Additionally, the ADC incorporates a variable resolution scheme, enhancing power efficiency and reducing data bandwidth. The ADC adaptively allocates more bits to the most significant portions of the ECG waveform while reducing the resolution for less critical segments by employing a time-based approach. This enables efficient data representation and reduces overall data transfer requirements, making this architecture more power-efficient. Implemented in 180 nm CMOS technology, the proposed ADC consumes only 498.6 mu W with a 1.8 V supply and achieves ENOB of 5.21 bits, SNDR of 31.76 dB, and SFDR of 44.31 dB. The sampling frequency of the proposed architecture changes from 64 Hz to 512 Hz, which is suitable for portable ECG monitoring applications. The FoM of the proposed work ranges from 100 to 262 fj/conversion-step for 64-512 Hz sampling rate, respectively.

关键词： adaptive sampling Time-based sampling ECG monitoring Low power Machine learning

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