Adaptive Sampling Method Research Articles

BICEPS: An improved characterization model for low- and intermediate-mass exoplanets

Context. The number of exoplanets with precise mass and radius measurements is constantly increasing thanks to novel ground- and space-based facilities such as HARPS, ESPRESSO, CHEOPS, and TESS. The accuracy and robustness of the planetary characterization largely depends on the quality of the data, but also requires a planetary structure model, capable of accurately modeling the interior and atmospheres of exoplanets over a large range of boundary conditions. Aims. Our goal is to provide an improved characterization model for planets with masses between 0.5 and 30 Earth masses, equilibrium temperatures below <2000 K, and a wide range of planetary compositions and physical phases. Methods. In this work, we present the Bayesian Interior Characterization of ExoPlanetS (BICEPS) model, which combines an adaptive Markov chain Monte Carlo sampling method with a state-of-the-art planetary structure model. BICEPS incorporates many recently developed equations of state suited for large ranges of pressures and temperatures, a description for solid and molten planetary cores and mantles, a gaseous envelope composed of hydrogen, helium, and water (with compositional gradients), and a non-gray atmospheric model. Results. We find that the usage of updated equations of state has a significant impact on the interior structure prediction. The impact varies, depending on the planetary composition. For dense rocky planets, BICEPS predicts radii a few percent different to prior internal structure models. For volatile rich planets, we find differences of 10% or even larger. When applying BICEPS to a particular exoplanet, TOI-130 b, we inferred a 25% larger water mass fraction and a 15% smaller core than previous models. Conclusions. The presented exoplanet characterization model is a robust method applicable over a large range of planetary masses, compositions, and thermal boundary conditions. We show the importance of implementing state-of-the-art equations of state for the encountered thermodynamic conditions of exoplanets. Hence, using BICEPS improves the predictive strength of the characterization process compared to previous methods.

Research on Adaptive Cluster Sampling Method Based on PPS

Research on Adaptive Cluster Sampling Method Based on PPS

Distance-Aware Learning for Inductive Link Prediction on Temporal Networks.

Inductive link prediction on temporal networks aims to predict the future links associated with node(s) unseen in the historical timestamps. Existing methods generate the predictions mainly by learning node representation from the node/edge attributes as well as the network dynamics or by measuring the distance between nodes on the temporal network structure. However, the attribute information is unavailable in many realistic applications and the structure-aware methods highly rely on nodes' common neighbors, which are difficult to accurately detect, especially in sparse temporal networks. Thus, we propose a distance-aware learning (DEAL) approach for inductive link prediction on temporal networks. Specifically, we first design an adaptive sampling method to extract temporal adaptive walks for nodes, increasing the probability of including the common neighbors between nodes. Then, we design a dual-channel distance measuring component, which simultaneously measures the distance between nodes in the embedding space and on the dynamic graph structure for predicting future inductive edges. Extensive experiments are conducted on three public temporal network datasets, i.e., MathOverflow, AskUbuntu, and StackOverflow. The experimental results validate the superiority of DEAL over the state-of-the-art baselines in terms of accuracy, area under the ROC curve (AUC), and average precision (AP), where the improvements are especially obvious in scenarios with only limited data.

An Adaptive Sampling Method Based on Expected Improvement Function and Residual Gradient in PINNs

An Adaptive Sampling Method Based on Expected Improvement Function and Residual Gradient in PINNs

Research on Adaptive Cluster Sampling Method based on PPS

This paper introduces the principle of PPS-based adaptive cluster sampling method and its modified HH estimator and HT estimator calculation method. It compares PPS-based adaptive cluster sampling method with SRS sampling and SRS-based adaptive group. The difference between the group sampling and the advantages and scope of the PPS adaptive cluster sampling method are analyzed. According to the case analysis, the relevant conclusions are drawn: 1) The adaptive cluster sampling method is more accurate than the SRS sampling; 2) SRS adaptive The HT estimator of the cluster sampling is more stable than the HH estimator; 3) The two estimators of the PPS adaptive cluster sampling method have little difference in the estimation of the population mean, but the HT estimator variance is smaller and more suitable; 4) PPS The HH estimator of adaptive cluster sampling is the same as the HH estimator of SRS adaptive cluster sampling, but the variance is larger and unstable.

Investigating adaptive sampling strategies for optimal building energy performance using artificial neural networks and kriging surrogate models

Recent studies highlight surrogate models as efficient alternatives to energy simulation tools, reducing computational burden. However, accurate metamodels often require extensive evaluations, leading to high computational costs. Adaptive sampling approaches offer a promising solution by constructing accurate surrogate models at a lower computational expense compared to static sampling. The selection of an appropriate adaptive sampling criterion is crucial in sequential sampling strategies, as it determines how new sample points are chosen based on previous iterations. However, concerns remain regarding the impact of the sampling infill criterion on surrogate model accuracy. Additionally, a lack of studies exists comparing artificial neural networks (ANN) and Kriging models in terms of potential computational improvements through sequential sampling. This study aims to explore the potential of adaptive sampling strategies to estimate optimal building energy consumption within a reasonable computational budget, thus reducing the number of energy simulation model runs required. Furthermore, a comprehensive comparison between sequential sampling techniques employing ANN-based and Kriging-based surrogate models is conducted. Four different sampling criteria are evaluated to understand the relationship between sampling strategies and accuracy. The findings contribute to a more efficient resolution of building optimization problems. The results indicate that the choice of a sampling strategy depends on the model's requirements and the specific problem. Each strategy has distinct strengths and weaknesses, necessitating careful consideration of the model's needs. Selecting the appropriate sampling strategy improves model accuracy and reduces training time. Adaptive sampling methods yield significant improvements, saving 25 %–68 % of simulation samples compared to static approaches.

Adaptive Sampling Methods for Molecular Dynamics in the Era of Machine Learning.

Molecular dynamics (MD) simulations are fundamental computational tools for the study of proteins and their free energy landscapes. However, sampling protein conformational changes through MD simulations is challenging due to the relatively long time scales of these processes. Many enhanced sampling approaches have emerged to tackle this problem, including biased sampling and path-sampling methods. In this Perspective, we focus on adaptive sampling algorithms. These techniques differ from other approaches because the thermodynamic ensemble is preserved and the sampling is enhanced solely by restarting MD trajectories at particularly chosen seeds rather than introducing biasing forces. We begin our treatment with an overview of theoretically transparent methods, where we discuss principles and guidelines for adaptive sampling. Then, we present a brief summary of select methods that have been applied to realistic systems in the past. Finally, we discuss recent advances in adaptive sampling methodology powered by deep learning techniques, as well as their shortcomings.

Satellite–Terrestrial Collaborative Object Detection via Task-Inspired Framework

Recently, buoyed by advances in the space industry, low Earth orbit (LEO) satellites have become an important part of the Internet of Things (IoT). LEO satellites have entered the era of a big data link with IoT, how to deal with the data from the satellite IoT is a problem worthy of consideration. Conventional object detection method in optical remote sensing simply transmits the raw data to the ground. However, it ignores the properties of the images and the connection with the downstream task. To obtain efficient data transmission and accurate object detection, we propose a task-inspired satellite-terrestrial collaborative object detection framework called STCOD. It detects regions of interest (ROIs) and adopts a block-based adaptive sampling method to compress the background (BG) in optical remote sensing images by introducing satellite edge computing (SEC) on satellites. The STCOD framework also sets the transmission priority of image blocks according to their contributions to the task and uses fountain code to ensure the reliable transmission of important image blocks. We build a whole software simulation framework to validate our method, including the satellite module, the transmission module, and the terrestrial module. Extensive experimental results show that the STCOD framework can reduce the amount of downlink data decreased by 50.04% while losing the detection accuracy by 0.54%. In our simulated satellite-terrestrial link, the STCOD framework can reduce the number of satellite-to-terrestrial transmissions by half. When the packet loss rate is between 5% and 20%, the detection accuracy is lost only 0.05% to 0.5%.

A construction strategy of Kriging surrogate model based on Rosenblatt transformation of associated random variables and its application in groundwater remediation

When using simulation-optimization models for optimizing the design of groundwater pumping-treatment plans for pollution, building a surrogate model for the numerical simulation model has become an effective means of overcoming the computational load of such models. However, previous studies often treated pumping time as a single optimization variable, leading to unnecessary excessive pumping. This paper considers the location, pumping rate, start time, and end time of each candidate pumping well as optimization variables, and proposes a Rosenblatt-transform-based optimal Latin hypercube sampling method for the associated random variables to ensure that the start time is less than or equal to the end time. This method is coupled with an adaptive sampling method based on batch local optimal solutions to construct a dynamic adaptive Kriging surrogate model for the numerical model, ensuring that the true value of the optimal remediation scheme is not lost. The results show that, at the final stage of remediation, the pollutant concentration in the 4 scenarios achieves comprehensive compliance. However, when considering the minimization of remediation costs as the evaluation criterion, the remediation scheme in scenario 1 (the pumping start and end times are independent optimization variables for all candidate pumping wells) is optimal. In the optimization design of groundwater pumping-treatment plans, the pumping wells should be arranged in the midstream and downstream regions of the contaminant plume and parallel to the regional flow direction. This paper provides a method reference for the construction and adaptive updating of surrogate models involving associated random variables, as well as guidance for the dynamic optimization of groundwater pumping and treatment at contaminated sites.

Adaptive Latin Hypercube Sampling for a Surrogate-Based Optimization with Artificial Neural Network

A significant number of sample points are often required for surrogate-based optimization when utilizing process simulations to cover the entire system space. This necessity is particularly pronounced in complex simulations or high-dimensional physical experiments, where a large number of sample points is essential. In this study, we have developed an adaptive Latin hypercube sampling (LHS) method that generates additional sample points from areas with the highest output deviations to optimize the required number of samples. The surrogate model used for the optimization problem is artificial neural networks (ANNs). The standard for measuring solution accuracy is the percent error of the optimal solution. The outcomes of the proposed algorithm were compared to those of random sampling for validation. As case studies, we chose three different chemical processes to illustrate problems of varying complexity and numbers of variables. The findings indicate that for all case studies, the proposed LHS optimization algorithm required fewer sample points than random sampling to achieve optimal solutions of similar quality. To extend the application of this methodology, we recommend further applying it to fields beyond chemical engineering and higher-dimensional problems.

Adaptive Sampling and Quick Anomaly Detection in Large Networks

The monitoring of data streams with a network structure have drawn increasing attention due to its wide applications in modern process control. In these applications, high-dimensional sensor nodes are interconnected with an underlying network topology. In such a case, abnormalities occurring to any node may propagate dynamically across the network and cause changes of other nodes over time. Furthermore, high dimensionality of such data significantly increased the cost of resources for data transmission and computation, such that only partial observations can be transmitted or processed in practice. Overall, how to quickly detect abnormalities in such large networks with resource constraints remains a challenge, especially due to the sampling uncertainty under the dynamic anomaly occurrences and network-based patterns. In this paper, we incorporate network structure information into the monitoring and adaptive sampling methodologies for quick anomaly detection in large networks where only partial observations are available. We develop a general monitoring and adaptive sampling method and further extend it to the case with memory constraints, both of which exploit network distance and centrality information for better process monitoring and identification of abnormalities. Theoretical investigations of the proposed methods demonstrate their sampling efficiency on balancing between exploration and exploitation, as well as the detection performance guarantee. Numerical simulations and a case study on power network have demonstrated the superiority of the proposed methods in detecting various types of shifts. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Continuous monitoring of networks for anomalous events is critical for a large number of applications involving power networks, computer networks, epidemiological surveillance, social networks, etc. This paper aims at addressing the challenges in monitoring large networks in cases where monitoring resources are limited such that only a subset of nodes in the network is observable. Specifically, we integrate network structure information of nodes for constructing sequential detection methods via effective data augmentation, and for designing adaptive sampling algorithms to observe suspicious nodes that are likely to be abnormal. Then, the method is further generalized to the case that the memory of the computation is also constrained due to the network size. The developed method is greatly beneficial and effective for various anomaly patterns, especially when the initial anomaly randomly occurs to nodes in the network. The proposed methods are demonstrated to be capable of quickly detecting changes in the network and dynamically changes the sampling priority based on online observations in various cases, as shown in the theoretical investigation, simulations and case studies.

Hierarchical Bayesian pharmacometrics analysis of Baclofen for alcohol use disorder

Alcohol use disorder (AUD), also called alcohol dependence, is a major public health problem, affecting almost of the world’s population. Baclofen, as a selective receptor agonist, has emerged as a promising drug for the treatment of AUD. However, the inter-trial, inter-individual and residual variability in drug concentration over time in a population of patients with AUD is unknown. In this study, we use a hierarchical Bayesian workflow to estimate the parameters of a pharmacokinetic (PK) population model from Baclofen administration to patients with AUD. By monitoring various convergence diagnostics, the probabilistic methodology is first validated on synthetic longitudinal datasets and then applied to infer the PK model parameters based on the clinical data that were retrospectively collected from outpatients treated with oral Baclofen. We show that state-of-the-art advances in automatic Bayesian inference using self-tuning Hamiltonian Monte Carlo (HMC) algorithms provide accurate and decisive predictions on Baclofen plasma concentration at both individual and group levels. Importantly, leveraging the information in prior provides faster computation, better convergence diagnostics, and substantially higher out-of-sample prediction accuracy. Moreover, the root mean squared error as a measure of within-sample predictive accuracy can be misleading for model evaluation, whereas the fully Bayesian information criteria correctly select the true data generating parameters. This study points out the capability of non-parametric Bayesian estimation using adaptive HMC sampling methods for easy and reliable estimation in clinical settings to optimize dosing regimens and efficiently treat AUD.

Efficient sampling method based on co-kriging for free-form surface measurement

Numerous studies on sampling methods have been conducted to balance the inspection efficiency and accuracy of coordinate measuring machines (CMMs). However, almost all existing methods only take into account the information from the processed surface during the reconstruction process. To address this limitation, an adaptive sampling method based on co-kriging method is proposed, according to the characteristics of the interpolation reconstruction method, we treat certain feature points on the surface as anchor points and determine the distribution of other measurement points through iteration. In the reconstruction process, we employ the co-kriging interpolation method and combine extra information of the model surface to enhance the reconstruction accuracy. Through simulations and actual measurements of processed surfaces, we compare the proposed method with other algorithms. The results indicate that our method is particularly suited for complex surfaces with a limited number of measurement points, and can substantially reduce the reconstruction error when the number of measurement points is the same.

Generating and validating cluster sampling matrices for model-free factor screening

Morris’ elementary effect-based screening (MM) has been widely used in a variety of domains to identify a few important factors among many possible ones. In MM, cluster sampling offers substantial computational savings over non-cluster sampling, but it remains a challenge to construct a cluster sampling matrix that generates any particular number of elementary effects for each factor. In this paper, we thoroughly address this issue. We uncover the mathematical association between distinct block sampling matrices within the complete cluster sampling matrix, by introducing a “dummy” sub-block matrix. By leveraging this property, we propose an easy-to-implement adaptive cluster sampling (ACS) method that is capable of identifying the appropriate sub-block sampling matrix to use. Its advantage over existing brute-force methods is that it can provide easy-to-obtain cluster sampling matrices, and it can be applied to computational model with any number of factors given a prior. We demonstrate the attractive properties of ACS using analytical proofs and simulation experiments. We show the robustness of ACS via a real-world case study. Our code for the algorithm is available online.

The development of complex engineering models using artificial neural network-based proxy models for life cycle assessments of energy systems

The energy industry has been using life cycle assessment (LCA) to determine the environmental impact of projects. Obtaining accurate data of certain industrial activities requires complex engineering models that have long computing times, are difficult for non-experts to use, and may contain confidential data. This work examines using proxy models based on quadratic and artificial neural network (ANN) regression to create an accurate, easy-to-use, black-box model that can be easily shared. Generating target values from the engineering software needed for training can be time-consuming, hence, adaptive sampling methods are examined (random, spread, high error, and the combo method [50/50 random/high error]). Two case studies were examined: a transportation fuel LCA of Maya, Bow River, and mined bitumen crude oils; and an LCA of a natural gas transmission pipeline (NGTL). This work found that ANN proxy models are more accurate than quadratic regression, and the high error sampling method reduced the maximum error but increased the average error. The combo and high error methods using 3000 to 4000 samples achieved similar maximum errors to the random method using 10,000 samples. Because of uncertainty in LCA input values, reducing average error is less valuable than reducing extreme errors. For the NGTL case study, the ANN model was able to reduce the average and max error by 67% and 68%, respectively, while using 35% fewer coefficients; ANN models are more appropriate for complex nonlinear models.

Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving partial differential equations with sharp solutions

We consider solving the forward and inverse partial differential equations (PDEs) which have sharp solutions with physics-informed neural networks (PINNs) in this work. In particular, to better capture the sharpness of the solution, we propose the adaptive sampling methods (ASMs) based on the residual and the gradient of the solution. We first present a residual only-based ASM denoted by ASM I. In this approach, we first train the neural network using a small number of residual points and divide the computational domain into a certain number of sub-domains, then we add new residual points in the sub-domain which has the largest mean absolute value of the residual, and those points which have the largest absolute values of the residual in this sub-domain as new residual points. We further develop a second type of ASM (denoted by ASM II) based on both the residual and the gradient of the solution due to the fact that only the residual may not be able to efficiently capture the sharpness of the solution. The procedure of ASM II is almost the same as that of ASM I, and we add new residual points which have not only large residuals but also large gradients. To demonstrate the effectiveness of the present methods, we use both ASM I and ASM II to solve a number of PDEs, including the Burger equation, the compressible Euler equation, the Poisson equation over an L-shape domain as well as the high-dimensional Poisson equation. It has been shown from the numerical results that the sharp solutions can be well approximated by using either ASM I or ASM II, and both methods deliver much more accurate solutions than the original PINNs with the same number of residual points. Moreover, the ASM II algorithm has better performance in terms of accuracy, efficiency, and stability compared with the ASM I algorithm. This means that the gradient of the solution improves the stability and efficiency of the adaptive sampling procedure as well as the accuracy of the solution. Furthermore, we also employ the similar adaptive sampling technique for the data points of boundary conditions (BCs) if the sharpness of the solution is near the boundary. The result of the L-shape Poisson problem indicates that the present method can significantly improve the efficiency, stability, and accuracy.

Towards Reliable Item Sampling for Recommendation Evaluation

Since Rendle and Krichene argued that commonly used sampling-based evaluation metrics are ``inconsistent'' with respect to the global metrics (even in expectation), there have been a few studies on the sampling-based recommender system evaluation. Existing methods try either mapping the sampling-based metrics to their global counterparts or more generally, learning the empirical rank distribution to estimate the top-K metrics. However, despite existing efforts, there is still a lack of rigorous theoretical understanding of the proposed metric estimators, and the basic item sampling also suffers from the ``blind spot'' issue, i.e., estimation accuracy to recover the top-K metrics when K is small can still be rather substantial. In this paper, we provide an in-depth investigation into these problems and make two innovative contributions. First, we propose a new item-sampling estimator that explicitly optimizes the error with respect to the ground truth, and theoretically highlights its subtle difference against prior work. Second, we propose a new adaptive sampling method that aims to deal with the ``blind spot'' problem and also demonstrate the expectation-maximization (EM) algorithm can be generalized for such a setting. Our experimental results confirm our statistical analysis and the superiority of the proposed works. This study helps lay the theoretical foundation for adopting item sampling metrics for recommendation evaluation and provides strong evidence for making item sampling a powerful and reliable tool for recommendation evaluation.

Adaptive cluster sampling based on balanced sampling plan excluding contiguous units

The method of adaptive cluster sampling (ACS) is useful for the surveys when the characteristic of interest is sparsely distributed but highly aggregated. In ACS, the initial sample is selected by simple random sampling (SRS). The final sample is obtained by adapting the nearby units which satisfy the pre-specified condition. This sampling can further be improved when the appropriate control is imposed to select the initial sample. In this article, Balanced Sampling plan Excluding Contiguous units (BSEC plan) has been used to select the initial sample for estimating population mean in ACS. The purpose is to ignore non preferred or contiguous units and include more preferred units in the final sample. The unbiased estimators of population mean based on the initial sample and modified Hansen-Hurwitz (HH) type estimators are discussed. It has been illustrated theoretically and empirically that the modified HH type estimator based on BSEC plan is more precise than that based on SRS. The proposed method provides the opportunity to select the non contiguous units in initial sample, which makes the design more informative and efficient in comparison to ACS. The proposed design may be helpful in improving the ACS estimates in many real-life situations.

A multi-mode failure boundary exploration and exploitation framework using adaptive kriging model for system reliability assessment

Various adaptive reliability analysis methods based on surrogate models have recently been developed. A multi-mode failure boundary exploration and exploitation framework (MFBEEF) was proposed for system reliability assessment using the adaptive kriging model based on sample space partitioning to reduce computational cost and use the characteristics of the failure boundary in multiple failure mode systems. The efficiency of the adaptive construction of kriging model can be improved by using the characteristics of the center sample of the small space to represent the characteristics of all samples in the small space. This method proposes a failure boundary exploration and exploitation strategy and a convergence criterion based on the maximum failure probability error for a system with multiple failure modes to adaptively approximate the failure boundary of a system with multiple failure modes. A multiple-failure-mode learning function was used to identify the optimal training sample to gradually update the kriging model during the failure boundary exploration and exploitation stages. In addition, a complex failure boundary-oriented adaptive hybrid importance sampling method was developed to improve the applicability of the MFBEEF method to small failure probability assessments. Finally, the MFBEEF method was proven to be effective using five system reliability analysis examples: a series system, a parallel system, a series–parallel hybrid system, a multi-dimensional series system with multiple failure modes, and an engineering problem with multiple implicit performance functions.

Multi-fidelity surrogate-based optimization for microfluidic concentration gradient generator design

PurposeThis paper aims to present a multi-fidelity surrogate-based optimization (MFSBO) method for computationally accurate and efficient design of microfluidic concentration gradient generators (µCGGs).Design/methodology/approachCokriging-based multi-fidelity surrogate model (MFSM) is constructed to combine data with varying fidelities and computational costs to accelerate the optimization process and improve design accuracy. An adaptive sampling approach based on parallel infill of multiple low-fidelity (LF) samples without notably adding computation burden is developed. The proposed optimization framework is compared with a surrogate-based optimization (SBO) method that relies on data from a single source, and a conventional multi-fidelity adaptive sampling and optimization method in terms of the convergence rate and design accuracy.FindingsThe results demonstrate that proposed MFSBO method allows faster convergence and better designs than SBO for all case studies with 49% more reduction in the objective function value on average. It is also found that parallel infill (MFSBO-4) with four LF samples, enables more robust, efficient and accurate designs than conventional multi-fidelity infill (MFSBO-1) that only adopts one LF sample during each iteration for more complex optimization problems.Originality/valueA MFSM based on cokriging method is constructed to utilize data with varying fidelities, accuracies and computational costs for µCGG design. A parallel infill strategy based on multiple infill criteria is developed to accelerate the convergence and improve the design accuracy of optimization. The proposed methodology is proved to be a feasible method for µCGG design and its computational efficiency is verified.