Fitting Algorithm Research Articles

Three-sensor 2ω method with multi-directional layout: A general methodology for measuring thermal conductivity of solid materials

Anisotropic thermal transport plays a key role in both theoretical study and engineering practice of heat transfer, but accurately measuring anisotropic thermal conductivity remains a significant challenge. To address this issue, we propose the three-sensor 2ω method in this study, which is capable of accurately measuring the isotropic or anisotropic thermal conductivity of solid materials. In this method, several three-sensor groups following the design guidelines are fabricated upon the sample along different characteristic directions, and each group consists of three parallel metal sensors with unequal widths and distances optimally designed based on sensitivity analysis. Among the three sensors, the outer two serve as AC heaters and the middle one as a DC detector. The 2ω voltage signals across the detector in each three-sensor group are measured, and then the data are processed by the proposed Intersection Method to derive thermal conductivities along directions of interest. The application of the detector's 2ω instead of the heater's 3ω voltage signals eliminates errors introduced by the uncertainties of thermal resistance in superficial structures (metal layer, insulation layer, interface, etc.). Meanwhile, by replacing the fitting algorithm with the Intersection Method, the local optimum trap of multivariate fitting is avoided. To verify the accuracy and reliability, four typical monocrystalline semiconductors, i.e., Si, GaN, AlN, and β-Ga2O3, are measured, and the results are consistent with the literature. This method will provide a comprehensive and versatile solution for the thermal conductivity measurements of solid materials.

Recovering non-Maxwellian particle velocity distribution functions from collective Thomson-scattered spectra

Collective optical Thomson scattering (TS) is a diagnostic commonly used to characterize plasma parameters. These parameters are typically extracted by a fitting algorithm that minimizes the difference between a measured scattered spectrum and an analytic spectrum calculated from the velocity distribution function (VDF) of the plasma. However, most existing TS analysis algorithms assume that the VDFs are Maxwellian, and applying an algorithm that makes this assumption does not accurately extract the plasma parameters of a non-Maxwellian plasma due to the effect of non-Maxwellian deviations on the TS spectra. We present new open-source numerical tools for forward modeling analytic spectra from arbitrary VDFs and show that these tools are able to more accurately extract plasma parameters from synthetic TS spectra generated by non-Maxwellian VDFs compared to standard TS algorithms. Estimated posterior probability distributions of fits to synthetic spectra for a variety of example non-Maxwellian VDFs are used to determine uncertainties in the extracted plasma parameters and show that correlations between parameters can significantly affect the accuracy of fits in plasmas with non-Maxwellian VDFs.

An Efficient and Robust Complex Weld Seam Feature Point Extraction Method for Seam Tracking and Posture Adjustment

To realize high-quality robotic welding, an efficient and robust complex weld seam feature point extraction method based on a deep neural network (Shuffle-YOLO) is proposed for seam tracking and posture adjustment. The Shuffle-YOLO model can accurately extract the feature points of butt joints, lap joints and irregular joints, and the model can also work well despite strong arc radiation and spatters. Based on the nearest neighbor algorithm and cubic B-spline curve fitting algorithm, the position and posture models of the complex spatially curved weld seams are established. The robot welding postures adjustment and high-precision seam tracking of complex spatially curved weld seams are realized. Experiments show that the method proposed in this paper can extract weld seam feature points quickly and robustly, which enables welding robots to accurately track the weld seams and adjust the welding torch postures simultaneously.

A computer-aided determining method for the myometrial infiltration depth of early endometrial cancer on MRI images

To classify early endometrial cancer (EC) on sagittal T2-weighted images (T2WI) by determining the depth of myometrial infiltration (MI) using a computer-aided diagnosis (CAD) method based on a multi-stage deep learning (DL) model. This study retrospectively investigated 154 patients with pathologically proven early EC at the institution between January 1, 2018, and December 31, 2020. Of these patients, 75 were in the International Federation of Gynecology and Obstetrics (FIGO) stage IA and 79 were in FIGO stage IB. An SSD-based detection model and an Attention U-net-based segmentation model were trained to select, crop, and segment magnetic resonance imaging (MRl) images. Then, an ellipse fitting algorithm was used to generate a uterine cavity line (UCL) to obtain MI depth for classification. In the independent test datasets, the uterus and tumor detection model achieves an average precision rate of 98.70% and 87.93%, respectively. Selecting the optimal MRI slices method yields an accuracy of 97.83%. The uterus and tumor segmentation model with mean IOU of 0.738 and 0.655, mean PA of 0.867 and 0.749, and mean DSC of 0.845 and 0.779, respectively. Finally, the CAD method based on the calculated MI depth reaches an accuracy of 86.9%, a sensitivity of 81.8%, and a specificity of 91.7% for early EC classification. In this study, the CAD method implements an end-to-end early EC classification and is found to be on par with radiologists in terms of performance. It is more intuitive and interpretable than previous DL-based CAD methods.

A method for obtaining maize phenotypic parameters based on improved QuickShift algorithm

Phenotype is an important bridge for studying the mechanism of “genotype-phenotype-environment”, and studying precise measurement of crop individual phenotypic information is of great significance for accelerating breeding processes and assisting in precision agriculture monitoring. The purpose of this study is to develop a clustering algorithm for corn population point clouds to accurately extract the three-dimensional morphology of individual crops. Firstly, this study used drones to obtain field corn data, reconstructed corn plant models based on the SfM algorithm, simplified and rotated point clouds, and extracted crop point clouds using methods based on color and distance thresholds. For density segmentation, this study improved the QuickShift method to segment individual corn crops. Color weights were added to the clustering density calculation process, and the original point cloud was projected onto a horizontal plane to calculate the density clustering core. Then, the remaining points were subjected to QuickShift clustering segmentation in 3D to obtain the point cloud data of individual corn plants. The use of cylindrical fitting and conditional Euclidean clustering algorithms to automatically segment point clouds of stems and leaves has achieved estimation of plant height, stem thickness, minimum bounding box volume per plant, and number of leaves. Finally, the true and measured values of plant height and stem thickness were compared. The results showed that the accuracy of the improved QuickShift method for segmentation was 93.10%, with an average measured plant height of 14.21 cm and an average stem diameter of 0.72 cm. The determination coefficient and root mean square error of the automatic and manual measurement results of plant height are 0.9186 and 0.5785 cm, respectively. The determination coefficients and root mean square errors of the automatic and manual measurement results of stem thickness are 0.8227 and 0.0574 cm, respectively. This study uses an improved QuickShift to cluster and segment corn point cloud data, achieving the segmentation of individual corn plants in the field, providing an automated, efficient, and low-cost solution for accurately measuring crop phenotypic information.

A Method for Convergent Deformation Analysis of a Shield Tunnel Incorporating B-Spline Fitting and ICP Alignment

The application of three-dimensional laser scanning technology in the field of tunnel deformation monitoring has changed the traditional measurement method. It provides an automated and intelligent solution for monitoring the geometric deformation of tunnel sections due to its high efficiency and independence from environmental influences. In this paper, based on B-spline fitting and iterative nearest point (ICP) alignment, the calculation of the difference between the radial distance and the design radius of a tunnel is transformed into a curve transformation that iterates over the nearest-neighbor points and calculates the difference in the distance between the corresponding points. The innovation of this paper is that the high-precision tunnel deformation monitoring method integrating B-spline fitting and ICP alignment can automatically compensate for the missing point clouds, is not affected by the point clouds of the tunnel inner and outer liner appendages, is more sensitive in the local deformation feedback and can be applied to a variety of tunnel shapes. The results indicate that our method maximally improves the accuracy of the horizontal convergence calculation by 28.6 mm and the accuracy of the vault settlement by 27.8 mm in comparison with the least squares circle fitting algorithm.

Modeling of novel circular gait motion through daisy sequence fitting algorithm in a vertical climbing snake robot

AbstractSnake robots can be used in multiple tasks, like, climbing, surveillance, pipe inspection, welding, and so forth. Current gaits of snake robots do not support rotation on a single horizontal plane in a circular fashion of a pole or tree at a fixed height. This makes snake robot extremely difficult to take the end effector to the desired target position and orientation. In addition, torque requirement of individual actuators or links of snake robot proportionally increases based on the number of links to be lifted while performing any task. In this paper, we propose a new gait called circular gait using the Daisy Sequence Fitting algorithm to solve the problems of circular rotation on a horizontal plane at a certain height with low torque. A sliding mode (SM) controller is implemented to achieve the circular gait's required position dynamically. Simulation results show that using the proposed circular gait, the end effector can be moved to any point on the circumference of the fixed horizontal plane of the pole or tree with a lesser torque. For the 0.5 kg module, circular gait moved the end effector to the target point using only 4.5 N m torque. The SM controller outperforms the proportional, integral, and derivative controllers in terms of response characteristics.

Analysis of mental health status assessment of college students based on interpolation and fitting algorithm

Abstract For common psychological problems among college students, the current mental health assessment methods are analyzed, and the least squares method of curve fitting is chosen for this paper. Based on the interrelationship between personality traits and mental health and the correlation between perceived campus climate and mental health inequalities, factors affecting the mental health status of college students were identified. The OLS and 2SLS algorithms, as well as the stepwise addition of control variables, were used for empirical testing, and a binary regression analysis model was established for individual UPI score values and mean UPI score values, and the results showed a peer effect coefficient of 0.654, which verified the existence of a significant positive peer effect of campus environment on college student’s mental health level.

Vision Sensing-Based Online Correction System for Robotic Weld Grinding

The service cycle and dynamic performance of structural parts are affected by the weld grinding accuracy and surface consistency. Because of reasons such as assembly errors and thermal deformation, the actual track of the robot does not coincide with the theoretical track when the weld is ground offline, resulting in poor workpiece surface quality. Considering these problems, in this study, a vision sensing-based online correction system for robotic weld grinding was developed. The system mainly included three subsystems: weld feature extraction, grinding, and robot real-time control. The grinding equipment was first set as a substation for the robot using the WorkVisual software. The input/output (I/O) ports for communication between the robot and the grinding equipment were configured via the I/O mapping function to enable the robot to control the grinding equipment (start, stop, and speed control). Subsequently, the Ethernet KRL software package was used to write the data interaction structure to realize real-time communication between the robot and the laser vision system. To correct the measurement error caused by the bending deformation of the workpiece, we established a surface profile model of the base material in the weld area using a polynomial fitting algorithm to compensate for the measurement data. The corrected extracted weld width and height errors were reduced by 2.01% and 9.3%, respectively. Online weld seam extraction and correction experiments verified the effectiveness of the system’s correction function, and the system could control the grinding trajectory error within 0.2 mm. The reliability of the system was verified through actual weld grinding experiments. The roughness, Ra, could reach 0.504 µm and the average residual height was within 0.21 mm. In this study, we developed a vision sensing-based online correction system for robotic weld grinding with a good correction effect and high robustness.

Para- and Transcellular Transport Kinetics of Nanoparticles across Lymphatic Endothelial Cells.

Lymphatic vessels have received significant attention as drug delivery targets, as they shuttle materials from peripheral tissues to the lymph nodes, where adaptive immunity is formed. Delivery of immune modulatory materials to the lymph nodes via lymphatic vessels has been shown to enhance their efficacy and also improve the bioavailability of drugs when delivered to intestinal lymphatic vessels. In this study, we generated a three-compartment model of a lymphatic vessel with a set of kinematic differential equations to describe the transport of nanoparticles from the surrounding tissues into lymphatic vessels. We used previously published data and collected additional experimental parameters, including the transport efficiency of nanoparticles over time, and also examined how nanoparticle formulation affected the cellular transport mechanisms using small molecule inhibitors. These experimental data were incorporated into a system of kinematic differential equations, and nonlinear, least-squares curve fitting algorithms were employed to extrapolate transport coefficients within our model. The subsequent computational framework produced some of the first parameters to describe transport kinetics across lymphatic endothelial cells and allowed for the quantitative analysis of the driving mechanisms of transport into lymphatic vessels. Our model indicates that transcellular mechanisms, such as micro- and macropinocytosis, drive transport into lymphatics. This information is crucial to further design strategies that will modulate lymphatic transport for drug delivery, particularly in diseases like lymphedema, where normal lymphatic functions are impaired.

Object Detection and Localization in GPR B- Scan images using Hyperbola Fitting Algorithm

Ground-penetrating radar [GPR] is a non-invasive method, that generates an electromagnetic wave to map a subsurface object. It finds applications in various fields such as civil / structural engineering, environmental, Geotechnical and Military applications. This research is conducted at the Civil Engineering laboratory, IISc, Bangalore to collect the experimental data, using the Mala Pro-ex ground-coupled antenna GPR system from Mala Geoscience. GPR technology emits electromagnetic pulses into the ground and captures the reflected echoes from the target,enabling a detailed visualization of the subsurface targets.This paper presents the Hyperbola fitting algorithm for accurate detection and localization of the underground objects such as metallic and nonmetallic. It involves several steps, including preprocessing, identifying hyperbolas, fitting hyperbolas, determining target locations and depths, and validating the results. Following the fitting of the hyperbola, the algorithm proceeds to estimate the target's position and depth using the hyperbola's parameters. These estimated values for the target location and depth are subsequently validated through a model generated using the gprMax numerical modeling tool and is also tested on experimental data. There are numerous iterations of hyperbola fitting algorithms, and their implementation varies according to the attributes of the GPR data and the subject of interest. This method provides significant advantages for engineers before excavation of the ground. The algorithm has been developed and implemented using MATLAB. It has been rigorously tested using both experimental data and simulated data generated through gprMax Numerical modeling tool. The algorithm achieves a minimum error of 0.02 in the lateral position (x) and 0.01 in the depth (Y) direction. These results demonstrate the algorithm's accuracy and reliability in identifying and locating buried objects.

High Precision and Stabilization PGC Demodulation Scheme for Fiber Optic Interferometric Sensors

Ellipse fitting algorithm (EFA) has attracted tremendous interest in phase demodulation schemes to eliminate nonlinear errors, however, there is an inherent drawback that the EFA fails to work when the desired phase signal is small. A high precision and stabilization phase generated carrier (PGC) demodulation scheme using hyper least squares (LS) fitting of ellipse is proposed for the first time, to our best knowledge. The contaminated quadrature signals obtained after mixing and low pass filtering are processed by a hyper LS based EFA to eliminate nonlinear errors. Hyper LS relies on algebraic distance minimization with a carefully chosen scale normalization and statistical bias is eliminated up to second order noise terms, which is far superior to the traditional LS. The temporary stability of the fitting result is used to resolve the drawback of the EFA. A module for judging the ellipse arc of quadrature signals is developed. The EFA is executed when the Lissajous figure is larger than 1/4 ellipse arc, otherwise the fitting result of the previous executed EFA is used to correct the ellipse. Experimental results show that the proposed scheme is highly precise and stable regardless of the desired signal amplitude. It has a high response linearity of 99.997%, a minimum detectable phase of 3.16 μrad and a large dynamic range of 123.52 dB @1 kHz & 1% THD.

Marriage between variable selection and prediction methods to model plant disease risk

Predicting the risk of a disease in a pathosystem based on a set of climatic variables usually requires handling a high number of input variables, many of which are often irrelevant and/or redundant. Building linear predictive models entails not only dimensionality issues but also the negative impact of multicollinearity. Several feature selection methods have proved to be efficient in both linear and non-linear models, regardless of those issues. However, in a machine learning (ML) context, it is necessary to evaluate these feature selection methods embedded into the model fitting algorithm to obtain the greatest accuracy. The aim of this work was to assess different combinations of variable selection methods with linear and non-linear predictors to fit climate-based models that predict the occurrence of a disease in a pathosystem. Four selection methods were compared: stepwise, which is frequently used in linear models, combined with VIF and p-value statistical criteria (Step+VIF+Pv), and other methods commonly used in ML: filter (F), genetic algorithm (GA), and Boruta (B). The disease risk predictors were constructed with a logistic linear regression model (LR) and the random forest (RF) algorithm, using all the available variables and the subgroups of variables selected by each feature selection method. Data from three pathosystems were processed: two involving Begomovirus –one in common bean (Phaseolus vulgaris L) and the other in soybean (Glycine max)– and the third one involving Mal de Rio Cuarto virus in maize (Zea mays L.). The data sets differed in sample size and number of variables. The accuracy of RF prediction did not vary among feature selection methods. Step+VIF+Pv was used to reduce the model outperformed the other feature selection methods in fitting LR. Our proposal suggests that the appropriate pairing of variable selection and prediction models would improve the modeling of plant disease risk.

A multi-physics coupled multi-scale transport model for CO2 sequestration and enhanced recovery in shale formation with fractal fracture networks

CO2 sequestration and enhanced gas recovery (CS-EGR) of shale formation involve complex multi-physics couplings, which are still not precisely captured by recent models. In this regard, a modified model fully considering the fractal characterization of fracture networks and multi-physics coupled transport behaviors in multi-scale shale formation is established. The fractal fracture networks are depicted based on the L-system theory with microseismic events (MSE), and embedded into the numerical modeling by an automatic fitting algorithm. As a development from the traditional transport model, the formulas among the pressure field, thermal field, and the molecular diffusion of binary gases are constructed based on the Chapman-Enskog theory. The effect of tortuosity and surface diffusion on gas transport efficiency can reach up to 5 %. The morphology parameters of induced fractures affect gas migration mainly by changing the size of SRV and the space between adjacent induced fractures, with their influence on CH4 production potentially as high as 20 %. Meanwhile, there are improved CH4 production, energy efficiency, and CO2 storage amounts, when the injection parameters are optimized. By optimizing the injection conditions (temperature, pressure), CH4 production can be increased by about 8 %.

A sensorless, Big Data based approach for phenology and meteorological drought forecasting in vineyards

A web-based app was developed and tested to provide predictions of phenological stages of budburst, flowering and veraison, as well as warnings for meteorological drought. Such predictions are especially urgent under a climate change scenario where earlier phenology and water scarcity are increasingly frequent. By utilizing a calibration data set provided by 25 vineyards observed in the Emilia Romagna Region for two years (2021–2022), the above stages were predicted as per the binary event classification paradigm and selection of the best fitting algorithm based on the comparison between several metrics. The seasonal vineyard water balance was calculated by subtracting daily bare or grassed soil evapotranspiration (ETs) and canopy transpiration (Tc) from the initial water soil reservoir. The daily canopy water use was estimated through a multiple, non-linear (quadratic) regression model employing three independent variables defined as total direct light, vapor pressure deficit and total canopy light interception, whereas ETS was entered as direct readings taken with a closed-type chamber system. Regardless of the phenological stage, the eXtreme Gradient Boosting (XGBoost) model minimized the prediction error, which was determined as the root mean square error (RMSE) and found to be 5.6, 2.3 and 8.3 days for budburst, flowering and veraison, respectively. The accuracy of the drought warnings, which were categorized as mild (yellow code) or severe (red code), was assessed by comparing them to in situ readings of leaf gas exchange and water status, which were found to be correct in 9 out of a total of 14 case studies. Regardless of the geolocation of a vineyard and starting from basic in situ or online weather data and elementary vineyard and soil characteristics, the tool can provide phenology forecasts and early warnings of meteorological drought with no need for fixed, bulky and expensive sensors to measure soil or plant water status.

Determining the effect of cardiac blood volume on accuracy of uptake rate constants by simulation

Objective. There is great interest in better understanding coronary microvascular disease using mouse models. Typical quantification requires dynamic imaging to estimate the rate constant K 1 of the tracer moving from the blood into the myocardium. In addition to K 1, it is also desirable to determine blood volume fraction V, which if known allows for more accurate fitting of K 1. Our previously published kinetic modeling software did not consider the effect of V. To ensure a better fit of experimental data to the model for myocardial SPECT imaging, in this work we updated our kinetic modeling software to include a blood volume fraction V, which adds a fraction of the arterial activity concentration into the tissue concentration. Approach. The tissue and blood time-activity curves (TACs) used for fit input were generated using ideal equations with known values in MATLAB. This allowed post-fit results to be compared to known values to determine fit errors. Parameters that were varied in generating the TACs included blood volume fraction (0, 0.05, 0.1, 0.2 and 0.3), K 1 (0.5, 1.5, 2.5 ml min−1 g−1), frame length (1, 2, 5, 10, 15, 20 s), FWHM of the input Gaussian (10, 20, 40 s), and time of the injection peak relative to frame duration. Blood volume-fraction results have low error when blood volume is lowest, but results worsen as frame length and K 1 increase. Main results. We demonstrated that blood volume can be accurately determined, and also show how fit accuracy varies across TACs with different input properties. Significance. This information allows for robust use of the fitting algorithm and aids in understanding fit performance when used in animal studies.

Study on Connectivity Analysis and Injection–Production Optimization of Strong Heterogeneous Sandstone Reservoir Based on Connectivity Method

The D reservoir in the Bongor Basin, southern Chad, is highly heterogeneous. In the stage of waterflood development, the injected water is seriously channeled along the dominant channel, and the water drive effect becomes worse. At the same time, due to the strong edge and bottom water, the water flooding situation is aggravated, the water cut is increased, and the development efficiency is reduced. To accurately identify the inter-well connectivity relationship, we developed a reservoir inter-well connectivity model based on the principle of inter-well connectivity and dynamic production data and reservoir geological parameters. Thus, the plane water injection split coefficient and water injection efficiency of each reservoir layer were obtained. The results are in good agreement with the calculation results of inter-well connectivity through verification with field tracer interpretation. The practical application results show that the method can increase the annual output of oil by 1.3%, which has a good oil increase effect. In this study, a model of inter-well connectivity in multi-layer sandstone reservoirs was established for the first time. The production performance of the model injection–production well was optimized in real time by a historical fitting and production optimization algorithm and then applied to real reservoirs, so that it could effectively improve the oilfield development and optimize the injection–production structure.

Development and Calibration of a Vertical High-Speed Mueller Matrix Ellipsometer

In order to meet the requirements of dynamic monitoring from a bird’s eye view for typical rapidly changing processes such as mechanical rotation and photoresist exposure reaction, we propose a vertical high-speed Mueller matrix ellipsometer that consists of a polarization state generator (PSG) based on the time-domain polarization modulation and a polarization state analyzer (PSA) based on division-of-amplitude polarization demodulation. The PSG is realized using two cascaded photoelastic modulators, while the PSA is realized using a six-channel Stokes polarimeter. On this basis, the polarization effect introduced by switching the optical-path layout of the instrument from the horizontal transmission to the vertical transmission is fully considered, which is caused by changing the incidence plane. An in situ calibration method based on the correct definition of the polarization modulation and demodulation reference plane has been proposed, enabling the precise calibration of the instrument by combining it with a time-domain light intensity fitting algorithm. The measurement experiments of SiO2 films and an air medium prove the accuracy and feasibility of the proposed calibration method. After the precise calibration, the instrument can exhibit excellent measurement performance in the range of incident angles from 45° to 90°, in which the measurement time resolution is maintained at the order of 10 μs, the measurement accuracy of Mueller matrix elements is better than 0.007, and the measurement precision is better than 0.005.

New Cases of Superflares on Slowly Rotating Solar-type Stars and Large Amplitude Superflares in G- and M-type Main Sequence Stars

In our previous work, we searched for superflares on different types of stars while focusing on G-type dwarfs using entire Kepler data to study statistical properties of the occurrence rate of superflares. Using these new data, as a by-product, we found 14 cases of superflare detection on 13 slowly rotating Sun-like stars with rotation periods of 24.5–44 days. This result supports the earlier conclusion by others that the Sun may possibly undergo a surprise superflare. Moreover, we found 12 and seven new cases of detection of exceptionally large amplitude superflares on six and four main sequence stars of G- and M-type, respectively. No large-amplitude flares were detected in A, F or K main sequence stars. Here we present preliminary analysis of these cases. The superflare detection, i.e., an estimation of flare energy, is based on a more accurate method compared to previous studies. We fit an exponential decay function to flare light curves and study the relation between e-folding decay time, τ, versus flare amplitude and flare energy. We find that for slowly rotating Sun-like stars, large values of τ correspond to small flare energies and small values of τ correspond to high flare energies considered. Similarly, τ is large for small flare amplitudes and τ is small for large amplitudes considered. However, there is no clear relation between these parameters for large amplitude superflares in the main sequence G- and M-type stars, as we could not establish clear functional dependence between the parameters via standard fitting algorithms.

Block sparsity promoting algorithm for efficient construction of cluster expansion models for multicomponent alloys

Multicomponent alloys are gaining significance as drivers of technological breakthroughs especially in structural and energy storage materials. The vast configuration space of these materials prohibit computational modeling using first-principles based methods alone. The cluster expansion (CE) method is the most widely used tool for modeling configurational disorder in alloys. CE relies on machine learning algorithms to train Hamiltonians and uses first-principles calculated data as training sets. In this paper we present a new compressive sensing-based algorithm for the efficient construction of CE Hamiltonians of multicomponent alloys. Our algorithm constructs highly sparse and physically reasonable models from a carefully selected small training set of alloy structures. Compared to conventional fitting algorithms, the algorithm achieves more than 50% reduction in the training set size. The resultant sparse models can sample the configuration space at least 3 × faster. We demonstrate this algorithm on 4 different alloy systems, namely Ag–Au, Ag–Au–Cu, Ag–Au–Cu–Pd and (Ge,Sn)(S,Se,Te).The sparse CE models for these alloys can rapidly reproduce known ground state orderings and order-disorder transitions. Our method can truly enable high-throughput multicomponent alloy thermodynamics by reducing the cost associated with model construction and configuration sampling.