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Autonomous Ground Vehicles (AGVs) can transfer or load and unload material in industrial scenarios due to their flexibility and operability, freeing people from tedious and repetitive labour. However, the dynamic scene and the system model uncertainty reduce the parking accuracy of AGVs in industrial scenes, which seriously affects the autonomous operation robustness and accuracy. Using sliding mode parking control with trajectory planning based on iterative error compensation, this paper proposes a data-driven parking control-planning integration solution for AGVs in complex industrial scenes, which allows AGVs to park accurately and converge to the target parking site quickly. First of all, a data-driven discrete sliding mode controller has been developed to iteratively enhance parking accuracy and effectively rectify the target parking position. This controller showcases insensitivity towards disturbances encountered during the erratic iterative error compensation process, thereby ensuring rapid and asymptotic convergence of the parking error in industrial scenarios. Then, to achieve efficient and smooth planning with the target parking site constantly being corrected, an improved Bi-RRT based trajectory planning scheme is proposed by considering operational constraints and node expanding region division, which provides the trajectory that contributes to parking convergence for the proposed controller promptly. Finally, the efficiency of the proposed method is verified by real-world experiments with self-developed AGV in industrial scenes, and experimental results show that the proposed method achieves accurate parking control with efficient trajectory planning and rapid parking error convergence. [ABSTRACT FROM AUTHOR]

In the contemporary business landscape, Human Resources (HR) functions are undergoing a transformative shift fueled by the integration of data-driven decision-making processes. This paper presents a comprehensive review of analytics in HR, shedding light on its evolution, methodologies, and strategic implications. As organizations strive to leverage their human capital for competitive advantage, the adoption of analytics in HR has emerged as a pivotal driver of informed decision-making. The review begins by tracing the evolution of HR analytics, from basic reporting to advanced predictive analytics and machine learning applications. It explores the diverse methodologies employed in HR analytics, ranging from descriptive statistics to sophisticated algorithms that can predict employee behavior, attrition, and performance. The paper also delves into the challenges associated with implementing HR analytics, such as data privacy concerns, ethical considerations, and the need for upskilling HR professionals to interpret and utilize analytics effectively. Moreover, the strategic importance of data-driven decision-making in HR is emphasized throughout the review. By harnessing the power of HR analytics, organizations can gain actionable insights into workforce dynamics, enabling them to optimize recruitment processes, identify talent gaps, and enhance employee engagement. The strategic alignment of HR analytics with overall business objectives is highlighted as a key factor in realizing its full potential. Case studies and examples from diverse industries are incorporated to illustrate successful applications of HR analytics, showcasing its impact on organizational performance and efficiency. The paper concludes by outlining future trends in HR analytics, including the integration of artificial intelligence, continuous learning algorithms, and the evolving role of HR professionals in the era of data-driven decision making. Overall, this review serves as a valuable resource for HR practitioners, scholars, and business leaders seeking to navigate and capitalize on the transformative potential of analytics in HR. [ABSTRACT FROM AUTHOR]

Structural damage is a prevalent issue in long-term operations of harbor terminals. Addressing the lack of transparency in terminal infrastructure components, the limited integration of sensor monitoring data, and the insufficient support for feedback on service performance, we propose a novel digital twin system construction methodology tailored for the long-term monitoring of port terminals. This study elaborates on the organization and processing of foundational geospatial data, sensor monitoring information, and oceanic hydrometeorological data essential for constructing a digital twin of the terminal. By mapping relationships between physical and virtual spaces, we developed comprehensive dynamic and static models of terminal facilities. Employing a "particle model" approach, we visually represented oceanic and meteorological elements. Additionally, we developed a multi-source heterogeneous data fusion model to facilitate the rapid creation of data indexes for harbor elements under high concurrency conditions, effectively addressing performance issues related to scene-rendering visualization and real-time sensor data storage efficiency. Experimental validation demonstrates that this method enables the rapid construction of digital twin systems for port terminals and supports practical application in business scenarios. Data analysis and comparison confirm the feasibility of the proposed method, providing an effective approach for the long-term monitoring of port terminal operations. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

After the coronavirus disease 2019 (COVID-19) outbreak erupted, it swiftly spread globally and triggered a severe public health crisis in 2019. To contain the virus's spread, several countries implemented various lockdown measures. As the governments faced this unprecedented challenge, understanding the impact of lockdown policies became paramount. The goal of addressing the pandemic crisis is to devise prudent policies that strike a balance between safeguarding lives and maintaining economic stability. Traditional mathematical and statistical models for studying virus transmission only offer macro-level predictions of epidemic development and often overlook individual variations' impact, therefore failing to reflect the role of government decisions. To address this challenge, we propose an integrated framework that combines agent-based modeling (ABM) and deep Q-network (DQN) techniques. This framework enables a more comprehensive analysis and optimization of epidemic control strategies while considering real human behavior. We construct a pandemic simulator based on the ABM method, accurately simulating agents' daily activities, interactions, and the dynamic spread of the virus. Additionally, we employ a data-driven approach and adjust the model through real statistical data to enhance its effectiveness. Subsequently, we integrated ABM into a decision-making framework using reinforcement learning techniques to explore the most effective strategies. In experiments, we validated the model's effectiveness by simulating virus transmission across different countries globally. In this model, we obtained decision outcomes when governments focused on various factors. Our research findings indicate that our model serves as a valuable tool for decision-makers, enabling them to formulate prudent and rational policies. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Electric Power Science & Technology is the property of Changsha University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For discrete-time linear systems, by applying the advantages of data-driven methods and iterative learning control, this paper proposes three data-driven learning control algorithms based on the linearity of the system. For iterative learning control, if the initial state shift is zero in each iteration and the system matrix is known, the algorithm presented in this paper can ensure that the system achieve complete tracking in the entire interval through a finite number of iterations. If the initial state shift is arbitrary in each iteration and the system matrix is unknown, the algorithm proposed in this paper can ensure that the system achieve complete tracking at all points except the initial point through finite iterations. Even for systems with non-repetitive processes, if the system matrix is unknown, the method in this paper can ensure that the system achieves complete tracking at all points except a finite number of points in the initial stage. Finally, the effectiveness of the three algorithms is verified by three examples, and the superiority of the proposed algorithms is demonstrated by comparison. [ABSTRACT FROM AUTHOR]

Overflow is one of the complicated working conditions that often occur in the drilling process. If it is not discovered and controlled in time, it will cause gas invasion, kick, and blowout, which will bring inestimable accidents and hazards. Therefore, overflow identification and early warning has become a hot spot and a difficult problem in drilling engineering. In the face of the limitations and lag of traditional overflow identification methods, the poor application effect, and the weak mechanisms of existing models and methods, a method of series fusion of feature data obtained from physical models as well as sliding window and random forest machine learning algorithm models is proposed. The overflow identification and early warning model of managed pressure drilling based on a series fusion data-driven model is established. The research results show that the series fusion data-driven model in this paper is superior to the overflow identification effect of other feature data and algorithm models, and the overflow recognition accuracy on the test samples reaches more than 99%. In addition, when the overflow is identified, the overflow warning is performed through the pop-up window and feature information output. The research content provides guidance for the identification of drilling overflow and the method of model fusion. [ABSTRACT FROM AUTHOR]

This paper discusses the generalizability of a data-augmented turbulence model with a focus on the field inversion and machine learning approach. It is highlighted that the augmented model based on two-dimensional (2D) separated airfoil flows gives poor predictive capability for a different class of separated flows (NASA wall-mounted hump) compared to the baseline model due to extrapolation. We demonstrate a sensor-based approach to localize the data-driven model correction to tackle this generalizability issue. Furthermore, the applicability of the augmented model to a more complex aeronautical three-dimensional case, the NASA Common Research Model configuration, is studied. Observations on the pressure coefficient predictions and the model correction field suggest that the present 2D-based augmentation is to some extent applicable to a three-dimensional aircraft flow. [ABSTRACT FROM AUTHOR]

Dynamic compressors are widely used in many industrial sectors, such as air, land, and marine vehicle engines, aircraft environmental control systems (ECS), air-conditioning and refrigeration, gas turbines, gas compression and injection, etc. The data-driven formulas of mass flow rate and isentropic efficiency of dynamic compressors are required for the design, energy analysis, performance simulation, and control- and/or diagnosis-oriented dynamic simulation of such compressors and the related systems. This work develops data-driven models for predicting the performance of dynamic compressors, including empirical models for mass flow rate and isentropic efficiency, which have high prediction accuracy and broad application range. The performance maps of two multi-stage axial compressors of an aero engine and a centrifugal compressor of an aircraft ECS were chosen for evaluation of the existing empirical formulas and testing of the new models. There are 16 empirical models of mass flow rate and 14 empirical models of isentropic efficiency evaluated, and the results show that it is necessary to develop highly accurate empirical formulas both for mass flow rate and isentropic efficiency. With the data-driven method, two empirical models for mass flow rate and one for isentropic efficiency are developed. They are in general form, with some terms removable to make them simple while enhancing their applicability and prediction accuracy. The new models have much higher prediction accuracy than the best existing counterparts. The new mass flow rate models predict for the three compressors a mean absolute relative deviation (MAD) not greater than 1.3%, while the best existing models all have MAD > 2.0%. The new efficiency model predicts for the three compressors an MAD of 1.0%, 0.4%, and 1.9%, respectively, while the best existing model predicts for the three compressors an MAD of 1.8%, 0.8%, and 3.2%, respectively. [ABSTRACT FROM AUTHOR]

The long lasting demand for better turbulence models and the still prohibitively computational cost of high‐fidelity fluid dynamics simulations, like direct numerical simulations and large eddy simulations, have led to a rising interest in coupling available high‐fidelity datasets and popular, yet limited, Reynolds averaged Navier–Stokes simulations through machine learning (ML) techniques. Many of the recent advances used the Reynolds stress tensor or, less frequently, the Reynolds force vector as the target for these corrections. In the present work, we considered an unexplored strategy, namely to use the modeled terms of the Reynolds stress transport equation as the target for the ML predictions, employing a neural network approach. After that, we solve the coupled set of governing equations to obtain the mean velocity field. We apply this strategy to solve the flow through a square duct. The obtained results consistently recover the secondary flow, which is not present in the baseline simulations that used the κ−ϵ$$ \kappa -\epsilon $$ model. The results were compared with other approaches of the literature, showing a path that can be useful in the seek of more universal models in turbulence. [ABSTRACT FROM AUTHOR]

Copyright of Continue Education Research is the property of Harbin Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The data-driven transient stability assessment (TSA) of power systems can predict online real-time prediction by learning the temporal features before and after faults. However, the accuracy of the assessment is limited by the quality of the data and has weak transferability. Based on this, this paper proposes a method for TSA of power systems based on an improved extreme gradient boosting (XGBoost) model. Firstly, the gradient detection method is employed to remove noise interference while maintaining the original time series trend. On this basis, a focal loss function is introduced to guide the training of the XGBoost model, enhancing the deep exploration of minority class samples to improve the accuracy of the model evaluation. Furthermore, to improve the generalization ability of the evaluation model, a transfer learning method based on model parameters and sample augmentation is proposed. The simulation analysis on the IEEE 39-bus system demonstrates that the proposed method, compared to the traditional machine learning-based transient stability assessment approach, achieves an average improvement of 2.16% in evaluation accuracy. Specifically, under scenarios involving changes in topology structure and operating conditions, the accuracy is enhanced by 3.65% and 3.11%, respectively. Moreover, the model updating efficiency is enhanced by 14-15 times, indicating the model's transferable and adaptive capabilities across multiple scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Study region: Beijing Plain, China Study focus: The traditional zoning of groundwater systems relies on lithological characteristics and hydraulic connections. However, the influence of climate change and human activities has led to the emergence of diverse dynamic patterns within these systems. Additionally, the differences in groundwater dynamic characteristics are often ignored when modeling with machine learning methods. In this study, a new clustering method named GRA-CLU was proposed to classify dynamic types of groundwater. Then, regional models are built using LSTM based on the dynamic zoning, aiming to incorporate hydrogeological significance into the pure data-driven model.New hydrological insights for the region: The GRA-CLU method classified the study area into six groundwater dynamic types. Compared with the traditional hydrogeological units, the new zoning result identified two dynamic types that have never been considered: urban influence zone and mountain-plain junction zone where surface water interacts with groundwater. Through analysis, the significant rise of groundwater levels in 2021 was more influenced by heavy rainfall events rather than human activities. This study further explored the performance of LSTM regional models based on the six dynamic zones. The results indicated that the NSE of models increased by 5.7–50.0 %, the improvement was more obvious in zones with irregular fluctuations. The RMSE decreased by 31.5–59.8 %, particularly noticeable in zones with regular annual fluctuations and large samples.

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system. The data-driven forecasting methods are regarded as an effective solution. However, the inherent randomness and nonlinearity of wind power systems, along with the abundance of redundant information in measurement data, present challenges to forecasting methods. The integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced data-driven forecasting models. Focus on the seasonal variation characteristics of wind energy, a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is proposed. The effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

The mechanical properties of basalt fiber-reinforced polymer (BFRP) concrete structures in corrosive environments are largely dependent on the bonding performance between the BFRP reinforcement and concrete. An accurate and convenient method for calculating the bonding strength is crucial for engineering design. However, experimental methods and existing empirical models are insufficient to encompass the intricate interdependencies among the bonding factors. This study proposed a machine learning (ML)-based bonding strength prediction method, which utilizes random forests (RF) and adaptive boosting (AdaBoost) algorithms to predict the interfacial bond strength of BFRP reinforcement in corroded concrete. The model was trained on a dataset of 355 samples, effectively quantifying the relationships between the BFRP reinforcement parameters, concrete parameters, corrosion factors, and bonding strength. The performance of the two algorithms was evaluated using R2, RMSE, and MAE indicators, and the prediction results were explained using the SHAP method. The results show that the predicted results of the machine learning model are highly consistent with the experimental results, with the AdaBoost model achieving an R2 of 0.925 on the test set, demonstrating high predictive performance. Among the various factors, the corrosion factor has the most significant impact on bonding strength, followed by concrete compressive strength and BFRP bar yield strength. Ultimately, the model was juxtaposed against traditional empirical formulas, affirming its efficacy and dependability. The research presents a novel approach to predicting bond strength in corroded BFRP bar-concrete systems.

This work focusses on identifying the optimal stacking sequence for composite tubes in mountain bike frames using a data-driven model combined with evolutionary algorithms. The objective is to find a frame that is sufficiently stiff while meeting the requirements of weight, strength, and minimum tube wall thickness. The decision variables are the ply winding angles and the ply thicknesses of each tube. The study performs designs for two load cases – Starting and Uphill – and explores two types of winding: the gradual winding of individual layers (1ply) and the winding of layers between predefined inner and outer layers with variable thicknesses (TW). Additionally, the design process is applied to frames made of isotropic materials, such as steel, aluminium, and titanium, using the same methodology to allow for comparison of results. The article demonstrates the successful application of this methodology to common sports equipment, suggesting its potential for beneficial use in other common composite frame structures.

Gastric cardia cancer is a high-incidence malignant tumour, which seriously endangers human health and life safety. The patient prognosis of gastric cardia cancer is affected by diet, physical condition, regional environment, medical history and other factors. Traditional prediction methods cannot fully reflect the prognosis characteristics and survival risks of all patients. Therefore, this paper proposes a data-driven method for the survival risk of cardiac cancer based on an adaptive particle swarm optimization algorithm (APSO) and a dynamic modular neural network (DMNN). First, the article uses density clustering to cluster 293 patients’ blood characteristics and generate different sub-networks. Second, the weight is calculated through the APSO algorithm and the sub-network output is obtained by the integration algorithm. At last, the effectiveness of this network is verified through a 50% cross-validation of training sets and test sets. The results show that the survival prediction based on the APSO-DMNN data-driven method shows good classification performance and accuracy.

The diverse forms of journalism adopted in the newsroom have redefined the news media landscape. This systematic review paper reviews fifty journal articles (2000 and 2021), including some of the new forms of journalism: data and network journalism, automated and virtual reality journalism, and augmented reality journalism. This paper fills the research gap by offering insights into these new forms of journalism cumulatively, contrary to the existing literature that discusses single forms. The findings reveal a shift in mainstream news media from traditional reporting methods. The adoption of new forms is part of the survival strategy in this competitive media market.

Abstract Functional coatings, including organic and inorganic coatings, play a vital role in various industries by providing a protective layer and introducing unique functionalities. However, its design often involves time‐consuming experimentation with multiple materials and processing parameters. To overcome these limitations, data‐driven approaches are gaining traction in materials science. In this paper, recent advances in data‐driven materials research and development (R&D) for functional coatings, highlighting the importance, data sources, working processes, and applications of this paradigm are summarized. It is begun by discussing the challenges of traditional methods, then introduce typical data‐driven processes. It is demonstrated how data‐driven approaches enable the identification of correlations between input parameters and coating performance, thus allowing for efficient prediction and design. Furthermore, carefully selected case studies are presented across diverse industries that exemplify the effectiveness of data‐driven methods in accelerating the discovery of new functional coatings with tailored properties. Finally, the emerging research directions, involving integrating advanced techniques and data from different sources, are addressed. Overall, this review provides an overview of data‐driven materials R&D for functional coatings, shedding light on its potential and future developments.

Abstract Ocean mesoscale eddies are often poorly represented in climate models, and therefore, their effects on the large scale circulation must be parameterized. Traditional parameterizations, which represent the bulk effect of the unresolved eddies, can be improved with new subgrid models learned directly from data. Zanna and Bolton (2020), https://doi.org/10.1029/2020gl088376 (ZB20) applied an equation‐discovery algorithm to reveal an interpretable expression parameterizing the subgrid momentum fluxes by mesoscale eddies through the components of the velocity‐gradient tensor. In this work, we implement the ZB20 parameterization into the primitive‐equation GFDL MOM6 ocean model and test it in two idealized configurations with significantly different dynamical regimes and topography. The original parameterization was found to generate excessive numerical noise near the grid scale. We propose two filtering approaches to avoid the numerical issues and additionally enhance the strength of large‐scale energy backscatter. The filtered ZB20 parameterizations led to improved climatological mean state and energy distributions, compared to the current state‐of‐the‐art energy backscatter parameterizations. The filtered ZB20 parameterizations are scale‐aware and, consequently, can be used with a single value of the non‐dimensional scaling coefficient for a range of resolutions. The successful application of the filtered ZB20 parameterizations to parameterize mesoscale eddies in two idealized configurations offers a promising opportunity to reduce long‐standing biases in global ocean simulations in future studies.

Abstract Thermal metamaterials are typically achieved by mixing different natural materials to realize effective thermal conductivities (ETCs) that conventional materials do not possess. However, the necessity for multifunctional design of metamaterials, encompassing both thermal and mechanical functionalities, is somewhat overlooked, resulting in the fixation of mechanical properties in thermal metamaterials designed within current research endeavors. Thus far, conventional methods have faced challenges in designing thermal metamaterials with configurable mechanical properties because of intricate inherent relationships among the structural configuration, thermal and mechanical properties in metamaterials. Here, a data‐driven approach is proposed to design a thermal metamaterial capable of seamlessly achieving thermal functionalities and harnessing the advantages of microstructural diversity to configure its mechanical properties. The designed metamaterial possesses thermal cloaking functionality while exhibiting exceptional mechanical properties, such as load‐bearing capacity, shearing strength, and tensile resistance, thereby affording mechanical protection for the thermal metadevice. The proposed approach can generate numerous distinct inverse design candidate topological functional cells (TFCs), designing thermal metamaterials with dramatic improvements in mechanical properties compared to traditional ones, which sets up a novel paradigm for discovering thermal metamaterials with extraordinary mechanical structures. Furthermore, this approach also paves the way for investigating thermal metamaterials with additional physical properties.

Research has highlighted the relevance of biological measures in explaining antisocial behavior, but the inclusion of such measures in clinical practice is lagging behind. According to the integrative biopsychosocial model, biological measures should be studied together with psychological and social-environmental factors. In this data-driven study, we applied this comprehensive model to explain non-violent and violent delinquency of 876 at-risk youth (715 male, 9–27 years), by combining nine biological (autonomic-nervous-system; endocrinological), nine psychological, and seven social-environmental measures. Using latent-class-regression analysis we uncovered four distinct psychologically-driven biological clusters, which differed in non-violent and violent delinquency-risk, moderated by social-environmental variables: a biological–psychopathic traits; low problem; high problem; and biological–reactive group. Individual vulnerabilities to (non-)violent delinquency depended on social-environmental context that differed between clusters. These findings highlight the importance of biological and psychological factors, in the context of social-environmental factors, in explaining (non)-violent delinquency.

The random fluctuations of wind energy and external grid voltage disturbances can both lead to serious voltage fluctuations and voltage deviations in the wind farm (WF). Voltage/reactive power control is an effective way to improve the voltage stability of WF. The existing research is based on complex physical models with prior parameter information, and the accuracy and calculation speed of the WF model are difficult to guarantee. To address this issue, this paper explores a decentralized optimal voltage control method for WF with deep learning-based (DL) data-driven modeling (DL-DOVC). A hybrid convolutional neural network (CNN)-Transformer architecture is proposed to establish the data-driven model of WF, leveraging its enhanced capabilities for extracting time series correlation, to learn complex patterns and dynamics from time series data. Then, we develop a fully decentralized voltage control method for WF to regulate the terminal bus voltage of wind turbines (WTs) within a feasible range. A DL-based data-driven predictive controller is specially designed to solve the established data-driven voltage optimization problem, it eliminates the necessity for frequent manual model maintenance and is suitable for real-time application. Additionally, the difficulty of WF decentralized optimal voltage control arises from the inability of local controllers to fully consider the impact of all non-local WTs on the local WT states. By designing the DL-based auxiliary state-feedback controller, the effects of non-local WTs are implicitly considered in the auxiliary feedback control law. A WF with 32*6.25 MW WTs is used to test the proposed DL-DOVC method. Simulation results show that the proposed DL-based model can efficiently learn and predict the WF dynamics under different operating scenarios. The near-global optimal voltage control performance is achieved only with local measurements by employing the DL-DOVC method.

We propose and demonstrate a computational framework to obtain data-driven surrogate constitutive models capturing the mechanical response of anisotropic brittle solids displaying progressive anisotropic damage. We train the constitutive models on data obtained from the analysis of a volume element of a material of interest; the data is generated by a constitutive model for braided composites, displaying a complex anisotropic damage evolution progressively transitioning from transversely isotropic to orthotropic. Training involves imposing six-dimensional random strain histories on the physical model and recording the histories of stress, strain and homogenised stiffness matrix of the material, obtained by a set of linear perturbation analyses. Supervised machine learning and dimensionality reduction are applied to the data and a structure for a surrogate model is proposed. The surrogate predicts the evolution of the stiffness of the solid consequent to an arbitrary imposed six-dimensional strain increment, thereby calculating the corresponding increment in stress. The model displays high accuracy and is able to reproduce the homogenised material's response via simple neural networks.