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Robust design optimization (RDO) is a potent methodology that ensures stable performance in designed products during their operational phase. However, there remains a scarcity of robust design optimization methods that account for the intricacies of multidisciplinary coupling. In this article, we propose a multidisciplinary robust design optimization (MRDO) framework for physical systems under sparse samples containing the extreme scenario. The collaboration model is used to select samples that comply with multidisciplinary feasibility, avoiding time-consuming multidisciplinary decoupling analyses. To assess the robustness of sparse samples containing the extreme scenario, linear moment estimation is employed as the evaluation metric. The comparative analysis of MRDO results is conducted across various sample sizes, with and without the presence of the extreme scenario. The effectiveness and reliability of the proposed method are demonstrated through a mathematical case, a conceptual aircraft sizing design, and an energy efficiency optimization of a hobbing machine tool. [ABSTRACT FROM AUTHOR]

Civil engineering design problems are inherently complex, characterized by iterative processes, multiple criteria, and time-consuming manual design work. Traditional methods often struggle to rapidly reach optimal designs, lacking guarantees of achieving optimality. With the advent of recent advances in artificial intelligence (AI), this study attempts to answer the research question: How AI algorithms can expedite the civil engineering design process, enhancing efficiency and accuracy in reaching optimal solutions with fewer resources. The research employs a Markov decision process-based AI framework, integrating configuration design and refinement in a unified approach. The methodology begins with the Markov decision-making process to mathematically model the design process, followed by reinforcement learning for automatic design and refinement of solutions. Applied to a planar truss bridge design problem, the AI design agent produced feasible truss designs under various constraints efficiently, demonstrating superior capability and flexibility. The results indicate an average improvement of 12% in accuracy and 88% in computational efficiency over traditional methods. The meaning and significance of the results lie in the innovative integration of Markov decision-making and reinforcement learning into a unified two-stage design framework, significantly advancing the body of knowledge in civil engineering design automation. The speed and accuracy of the AI design agent validate the feasibility of the proposed model and highlight its potential in effectively solving complex civil engineering design problems. The directions for follow-up research are suggested to extend this framework to a wider array of design challenges and to refine the AI agent's adaptability in more diverse design contexts. [ABSTRACT FROM AUTHOR]

The assessment of system performance and identification of failure mechanisms in complex engineering systems often requires the use of computation-intensive finite element software or physical experiments, which are both costly and time-consuming. Moreover, when accounting for uncertainties in the manufacturing process, material properties, and loading conditions, the process of reliability-based design optimization (RBDO) for complex engineering systems necessitates the repeated execution of expensive tasks throughout the optimization process. To address this problem, this paper proposes a novel methodology for RBDO. First, a multi-fidelity surrogate modeling strategy is presented, leveraging partially observed information (POI) from diverse sources with varying fidelity and dimensionality to reduce computational cost associated with evaluating expensive high-dimensional complex systems. Second, a multi-task surrogate modeling framework is proposed to address the concurrent evaluation of multiple constraints for each design point. The multi-task framework aids in the development of surrogate models and enhances the effectiveness of reliability analysis and design optimization. The proposed multi-fidelity multi-task machine learning model utilizes a Bayesian framework, which significantly improves the performance of the predictive model and provides uncertainty quantification of the prediction. Additionally, the model provides a highly accurate and efficient framework for reliability-based design optimization through knowledge sharing. The proposed method was applied to two design case studies. By incorporating POI from various sources, the proposed approach improves the accuracy and efficiency of system performance prediction, while simultaneously addressing the cost and complexity associated with the design of complex systems. [ABSTRACT FROM AUTHOR]

This article presents a novel three-dimensional topology optimization framework developed for 3D spatial packaging of interconnected systems using a geometric projection method (GPM). The proposed gradient-based topology optimization method simultaneously optimizes the locations and orientations of system components (or devices) and lengths, diameters, and trajectories of interconnects to reduce the overall system volume within the prescribed 3D design domain. The optimization is subject to geometric and physics-based constraints dictated by various system specifications, suited for a wide range of transportation (aerospace or automotive), heating, ventilation, air-conditioning, and refrigeration, and other complex system applications. The system components and interconnects are represented using 3D parametric shapes such as cubes, cuboids, and cylinders. These objects are then projected onto a three-dimensional finite element mesh using the geometric projection method. Sensitivities are calculated for the objective function (bounding box volume) with various geometric and physics-based (thermal and hydraulic) constraints. Several case studies were performed with different component counts, interconnection topologies, and system boundary conditions and are presented to exhibit the capabilities of the proposed 3D multi-physics spatial packaging optimization framework. [ABSTRACT FROM AUTHOR]

Neural networks have gained popularity for modeling complex non-linear relationships. Their computational efficiency has led to their growing adoption in optimization methods, including topology optimization. Recently, there have been several contributions toward improving derivatives of neural network outputs, which can improve their use in gradient-based optimization. However, a comparative study has yet to be conducted on the different derivative methods for the sensitivity of the input features on the neural network outputs. This paper aims to evaluate four derivative methods: analytical neural network's Jacobian, central finite difference method, complex step method, and automatic differentiation. These methods are implemented into density-based and homogenization-based topology optimization using multilayer perceptrons (MLPs). For density-based topology optimization, the MLP approximates Young's modulus for the solid isotropic material with penalization (SIMP) model. For homogenization-based topology optimization, the MLP approximates the homogenized stiffness tensor of a representative volume element, e.g., square cell microstructure with a rectangular hole. The comparative study is performed by solving two-dimensional topology optimization problems using the sensitivity coefficients from each derivative method. Evaluation includes initial sensitivity coefficients, convergence plots, and the final topologies, compliance, and design variables. The findings demonstrate that neural network-based sensitivity coefficients are sufficiently accurate for density-based and homogenization-based topology optimization. The neural network's Jacobian, complex step method, and automatic differentiation produced identical sensitivity coefficients to working precision. The study's open-source code is provided through a python repository. [ABSTRACT FROM AUTHOR]

The design representations of lattice structures are fundamental to the development of computational design approaches. Current applications of lattice structures are characterized by ever-growing demand on computational resources to solve difficult optimization problems or generate large datasets, opting for the development of efficient design representations which offer a high range of possible design variants, while at the same time generating design spaces with attributes suitable for computational methods to explore. In response, the focus of this work is to propose a parametric design representation based on crystallographic symmetries and investigate its implications for the computational design of lattice structures. The work defines design rules to support the design of functionally graded structures using crystallographic symmetries such that the connectivity between individual members in a structure with varying geometry is guaranteed and investigates how to use the parametrization in the context of optimization. The results show that the proposed parametrization achieves a compact design representation to benefit the computational design process by employing a small number of design variables to control a broad range of complex geometries. The results also show that the design spaces based on the proposed parametrization can be successfully explored using a direct search-based method. [ABSTRACT FROM AUTHOR]

Microfluidic mixers, a pivotal application of microfluidic technology, are primarily utilized for the rapid amalgamation of diverse samples within microscale devices. Given the intricacy of their design processes and the substantial expertise required from designers, the intelligent automation of microfluidic mixer design has garnered significant attention. This paper discusses an approach that integrates artificial neural networks (ANNs) with reinforcement learning techniques to automate the dimensional parameter design of microfluidic mixers. In this study, we selected two typical microfluidic mixer structures for testing and trained two neural network models, both highly precise and cost-efficient, as alternatives to traditional, time-consuming finite-element simulations using up to 10,000 sets of COMSOL simulation data. By defining effective state evaluation functions for the reinforcement learning agents, we utilized the trained agents to successfully validate the automated design of dimensional parameters for these mixer structures. The tests demonstrated that the first mixer model could be automatically optimized in just 0.129 s, and the second in 0.169 s, significantly reducing the time compared to manual design. The simulation results validated the potential of reinforcement learning techniques in the automated design of microfluidic mixers, offering a new solution in this field. [ABSTRACT FROM AUTHOR]

Flow-based microfluidic biochips are presently designed, verified, and physically laid out manually. Software automation, similar to that employed for integrated circuits in the semiconductor industry, will raise productivity and reduce the cost of using biochips in scientific experiments.

The article provides readers with a detailed overview of the AutoMedPrint technology, its advantages and opportunities, based on a number of studies and case examples. The AutoMedPrint system represents a significant advancement in the field of orthopedics, offering a comprehensive solution for the automated design and rapid production of personalized orthopedic and prosthetic devices through 3D printing technology. This paper details the system's capabilities and methodologies behind it, which include the design and manufacturing of various devices such as wrist hand orthoses (WHO) and ankle foot orthoses (AFO), among others. These devices are crafted using data obtained from precise anthropometric measurements, allowing for high customization to meet individual patient needs. The AutoMedPrint system enhances the quality of life for patients by providing devices that are not only functional and cost-effective but also rapidly produced, ensuring timely intervention. Case studies demonstrate the system's effectiveness in practical scenarios, highlighting its potential to revolutionize orthopedic care by integrating new materials and technologies that adapt to changing medical and patient requirements. The discussion extends to the lifecycle of the produced devices, emphasizing sustainability and continuous improvement, ensuring the system's relevance and efficacy in modern medical practice.

As modern electronic devices are increasingly miniaturized and integrated, their performance relies more heavily on effective thermal management. In this regard, two-phase cooling methods which capitalize on thin-film evaporation atop structured porous surfaces are emerging as potential solutions. In such porous structures, the optimum heat dissipation capacity relies on two competing objectives that depend on mass and heat transfer. Optimizing these objectives for effective thermal management is challenging due to the simulation costs and the high dimensionality of the design space which is often a voxelated microstructure representation that must also be manufacturable. We address these challenges by developing a data-driven framework for designing optimal porous microstructures for cooling applications. In our framework, we leverage spectral density functions to encode the design space via a handful of interpretable variables and, in turn, efficiently search it. We develop physics-based formulas to simulate the thermofluidic properties and assess the feasibility of candidate designs based on offline image-based analyses. To decrease the reliance on expensive simulations, we generate multi-fidelity data and build emulators to find Pareto-optimal designs. We apply our approach to a canonical problem on evaporator wick design and obtain fin-like topologies in the optimal microstructures which are also characteristics often observed in industrial applications. [ABSTRACT FROM AUTHOR]

Among the methods of 3D reconstruction, the automatic generation of 3D models from building documentation is one of the most accessible and inexpensive. For 30 years, researchers have proposed multiple methods to automatically generate 3D models from 2D drawings. This study compiles this research and discusses the different methods used to generate 3D models from 2D drawings. It offers a critical review of these methods, focusing on the coverage and completeness of the reconstruction process. This review allows us to identify the research gaps in the literature, and opportunities for improvement are identified for future research.

Among the methods of 3D reconstruction, the automatic generation of 3D models from building documentation is one of the most accessible and inexpensive. For 30 years, researchers have proposed multiple methods to automatically generate 3D models from 2D drawings. This study compiles this research and discusses the different methods used to generate 3D models from 2D drawings. It offers a critical review of these methods, focusing on the coverage and completeness of the reconstruction process. This review allows us to identify the research gaps in the literature, and opportunities for improvement are identified for future research. [ABSTRACT FROM AUTHOR]

In the early stages of the design process, designers explore opportunities by discovering unmet needs and developing innovative concepts as potential solutions. From a human-centered design perspective, designers must develop empathy with people to truly understand their experiences and needs. However, developing empathy is a complex and subjective process that relies heavily on the designer's empathic capability, and is often subject to the experiences of a small group of people. Therefore, the development of empathic understanding is intuitive, and the discovery of underlying needs can be serendipitous and unrepresentative. This paper aims to provide insights from artificial intelligence research to indicate the future direction of AI-driven human-centered design, considering the essential role of empathy. Specifically, we conduct an interdisciplinary investigation of research areas such as data-driven user research, empathic design, and artificial empathy. Based on this foundation, we discuss the role that artificial empathy can play in human-centered design and propose an artificial empathy framework for human-centered design. Building on the mechanisms behind empathy and insights from empathic design research, the framework aims to break down the rather complex and subjective process of developing empathic understanding into modules and components that can potentially be modeled computationally. Furthermore, we discuss the expected benefits of developing such systems and identify research opportunities to suggest future research efforts. [ABSTRACT FROM AUTHOR]

Incorporating style-related objectives into shape design has been centrally important to maximize product appeal. However, algorithmic style capture and reuse have not fully benefited from automated data-driven methodologies due to the challenging nature of design describability. This paper proposes an AI-driven method to fully automate the discovery of brand-related features. First, to tackle the scarcity of vectorized product images, this research proposes two data acquisition workflows: parametric modeling from small curve-based datasets, and vectorization from large pixel-based datasets. Second, this study constructs BIGNet, a two-tier Brand Identification Graph Neural Network, to learn from both scalar vector graphics' curve-level and chunk-level parameters. In the first case study, BIGNet not only classifies phone brands but also captures brand-related features across multiple scales, such as lens' location, as confirmed by AI evaluation. In the second study, this paper showcases the generalizability of BIGNet learning from a vectorized car image dataset and validates the consistency and robustness of its predictions given four scenarios. The results match the difference commonly observed in luxury versus economy brands in the automobile market. Finally, this paper also visualizes the activation maps generated from a convolutional neural network and shows BIGNet's advantage of being a more explainable style-capturing agent. [ABSTRACT FROM AUTHOR]

In many design automation applications, accurate segmentation and classification of 3D surfaces and extraction of geometric insight from 3D models can be pivotal. This paper primarily introduces a machine earning-based scheme that leverages graph neural networks for handling 3D geometries, specifically for surface classification. Our model demonstrates superior performance against two state-of-the-art models, PointNet + + and PointMLP, in terms of surface classification accuracy, beating both models. Central to our contribution is the novel incorporation of conformal predictions, a method that offers robust uncertainty quantification and handling with marginal statistical guarantees. Unlike traditional approaches, conformal predictions enable our model to ensure precision, especially in challenging scenarios where mistakes can be highly costly. This robustness proves invaluable in design applications, and as a case in point, we showcase its utility in automating the computational fluid dynamics meshing process for aircraft models based on expert guidance. Our results reveal that our automatically generated mesh, guided by the proposed rules by experts enabled through the segmentation model, is not only efficient but matches the quality of expert-generated meshes, leading to accurate simulations. [ABSTRACT FROM AUTHOR]

Machine learning is gaining prominence in mechanical design, offering cost-effective surrogate models to replace computationally expensive models. Nevertheless, concerns persist regarding the accuracy of these models, especially when applied to safety-critical products. To address this challenge, this study investigates methods to account for model prediction errors by incorporating epistemic uncertainty within surrogate models while managing aleatory uncertainty in input variables. The paper clarifies key aspects of modeling coupled epistemic and aleatory uncertainty when using surrogate models derived from noise-free training data. Specifically, the study concentrates on quantifying the impacts of coupled uncertainty in mechanical design through the development of numerical methods based on the concept of the most probable point. This method is particularly relevant for mechanical component design, where failure prevention holds paramount importance, and the probability of failure is low. It is applicable to design problems characterized by probability distributions governing aleatory and epistemic uncertainties in model inputs and predictions. The proposed method is demonstrated using shaft and beam designs as two illustrative examples. The results demonstrate the method's effectiveness in quantifying and mitigating the influence of coupled uncertainty in the design process. [ABSTRACT FROM AUTHOR]

In recent years, the field of structural DNA nanotechnology has advanced rapidly due to transformative design tools. Although these tools have been revolutionary, they still bear one overall limitation of requiring users to fully conceptualize their designs before designing. Recently, a simple computational casting technique was developed using generative optimization strategies to automate the DNA origami nanostructure design. This approach employs a shape annealing algorithm, which creates a formal language of honeycomb nanostructures with shape grammars and drives designs from the language toward a desired configuration using simulated annealing. This initial demonstration of the approach can generate novel scaffold routing schemes for creating solid or hollow structures constrained by the boundaries of polyhedral meshes. The results from the initial approach, particularly from the hollow structures, reveal a challenging design space. This simple technique generates novel scaffold routing schemes that do not replicate the overall polyhedral mesh shape and are limited in their ability to control scaffold path exploration in the design space. This paper demonstrates an approach for achieving different levels of consistent effective wall thicknesses and improving the quality of mesh coverage for hollow structures that can be tuned and optimized by introducing a more refined computational casting technique. We achieve these improvements through changes in the simulated annealing algorithm by adding a Hustin move set algorithm that dynamically adjusts the performance of the overall design and redefining how these hollow designs are articulated. This work illustrates how the technique can navigate a challenging design space to generate effective hollow designs. [ABSTRACT FROM AUTHOR]

This study aims to improve the prediction accuracy of the computer-aided engineering (CAE) model for crashworthiness performance evaluation at speeds beyond those defined by current regulations and public domain testing protocols. One way of achieving this is by integrating data from a few physical crash tests with the CAE data using machine learning models. In this study, two scenarios are investigated: (1) improving CAE model prediction accuracy using test data of a vehicle type that is the same as that of the CAE model; (2) improving CAE model prediction accuracy using test data from two different types of vehicles (e.g., two different sizes of SUVs). In the first scenario, a novel approach is proposed in the displacement domain (deceleration versus displacement) to enable data fusion to help recover the unmodeled physics in the CAE model. A nonlinear spring-mass model is used to simulate rigid-barrier vehicle frontal impact. A Gaussian process regression (GPR) model is then applied in conjunction with a Gaussian mixture model to capture the model bias of the nonlinear spring constant under a dynamic analysis scheme. In the second scenario, we propose a time-domain method (deceleration versus time) based on temporal convolutional network (TCN) and transfer learning. An initial TCN model is first trained by fusing CAE data with physical test data of the first vehicle type based on data augmentation. This data-augmented TCN model is then fine-tuned through transfer learning using CAE and test data of the second vehicle type. It leverages the domain-invariant representations of the two types of vehicles to enhance the CAE model prediction accuracy of the second vehicle type. Case studies are used to validate the proposed approaches and to demonstrate their efficacy in improving the prediction accuracy of the CAE models. [ABSTRACT FROM AUTHOR]

Continuous constraint satisfaction is prevalent in many science and engineering fields. When solving continuous constraint satisfaction problems, it is more advantageous for practitioners to derive all feasible regions (i.e., the solution space) rather than a limited number of solution points, since these feasible regions facilitate design concept generation and design tradeoff evaluation. Several central processing unit (CPU)-based branch-and-prune methods and geometric approximation methods have been proposed in prior research to derive feasible regions for continuous constraint satisfaction problems. However, these methods have not been extensively adopted in practice, mainly because of their high computational expense. To overcome the computational bottleneck of extant CPU-based methods, this paper introduces a GPU-based parallel region classification method to derive feasible regions for continuous constraint satisfaction problems in a reasonable computational time. Using interval arithmetic, coupled with the computational power of GPU, this method iteratively partitions the design space into many subregions and classifies these subregions as feasible, infeasible, and indeterminate regions. To visualize these classified regions in the design space, a planar visualization approach that projects all classified regions into one figure is also proposed. The GPU-based parallel region classification method and the planar visualization approach are validated through two case studies about the bird function and the welded beam design. These case studies show that the method and the approach can solve the continuous constraint satisfaction problems and visualize the results effectively and efficiently. A four-step procedure for implementing the method and the approach in practice is also outlined. [ABSTRACT FROM AUTHOR]

Given the foundational role of system requirements in design projects, designers can benefit from classifying, comparing, and observing connections between requirements. Manually undertaking these processes, however, can be laborious and time-consuming. Previous studies have employed Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art natural language processing (NLP) deep neural network model, to automatically analyze written requirements. Yet, it remains unclear whether BERT can sufficiently capture the nuances that differentiate requirements between and within design documents. This work evaluates BERT's performance on two requirement classification tasks (one inter-document and one intra-document) executed on a corpus of 1,303 requirements sourced from five system design projects. First, in the "parent document classification" task, a BERT model is fine-tuned to classify requirements according to their originating project. A separate BERT model is then fine-tuned on a "functional classification" task where each requirement is classified as either functional or nonfunctional. Our results also include a comparison with a baseline model, Word2Vec, and demonstrate that our model achieves higher classification accuracy. When evaluated on test sets, the former model receives a Matthews correlation coefficient (MCC) of 0.95, while the latter receives an MCC of 0.82, indicating BERT's ability to reliably distinguish requirements. This work then explores the application of BERT's representations, known as embeddings, to identify similar requirements and predict requirement change. [ABSTRACT FROM AUTHOR]

The challenges of conventional semiconducting electronics, such as dark silicon, memory wall, and stagnant clock frequency motivate the search for alternative computing technologies. Superconductive electronics (SCE) represents one of the most advanced beyond-CMOS technologies, with the potential of revolutionizing high-performance computing. In this review, the advancements of SCE over the past 25 years are presented. Novel logic families, circuit design techniques, and electronic design automation tools for SCE circuits are described. Eventually, future advancements in SCE are discussed from circuit and system design perspective. [ABSTRACT FROM AUTHOR]

Mechanical, electrical, and plumbing (MEP) system experts often have difficulties in converting their designs, in computer-aided design (CAD), into building information models (BIMs) owing to excessive repetitive work, lack of BIM skilled workers, frequent BIM recreations, etc. Therefore, this study aimed to formalize the CAD-to-BIM process of MEP systems, focusing on sanitary plumbing system, including soil, drain system, hot water supply, and cold water supply system. Each system's process is composed of a combination of the following five common modules: pipeline processing module, pipe connection module, initial level setting module, addition riser nipple module, and clash resolution module. Consequently, a prototype developed based on the formalized process produced a sanitary plumbing BIM model that is identical to the CAD drawings and has no clashes. This study contributes to the MEP BIM theory by identifying considerations for CAD-to-BIM conversion of sanitary plumbing system and formalizing the process reflecting them. [ABSTRACT FROM AUTHOR]

Designing power electronics converters for harsh environments is challenging due to the absence of components' performance under harsh conditions, the frequent transition and data-passing among various software, and the time-consuming and computationally heavy work flow. This paper promotes using design automation to address the aforementioned design challenges. The implementations include public-accessible component databases, automated co-action among circuit simulators and finite element simulations to perform electrical, electromagnetic and thermal co-design, and finally an exclusion-based work flow with hierarchical computation to reduce computational load. The theorized framework is exemplified on designing a real world 175 °C 1.5 kW Three-level Neutral-point-clamped dc-dc converter. A database containing the high-temperature characteristics of SiC MOSFETs and ferrites is established and shared through a web application with graphical user interface. In 310 min, the program, which includes computationally heavy 3D finite element simulation, delivers design output after evaluating the converter's electrical, electromagnetic and thermal performance under 10 million parameter sets. Finally, a 1.5 kW dc-dc converter prototype is built and tested in 175 °C ambient temperature to verify the quality of the design output.

Research on electromagnetic (EM) components is essential to enabling the design and optimization of such devices as antennas and filters, leading to improved functionality, reduced costs, and enhanced overall performance. This paper presents an overview of recent developments in optimization and design automation techniques for EM-component design and modeling. Limitations of conventional optimization methods are discussed, while the need for novel machine learning techniques capable of handling multiple objectives and large design spaces is highlighted. In this study, existing methods in the literature are reviewed from four viewpoints: structural view, algorithm view, component view, and application view. Different schemes in distinct design stages or applications are examined with advantages and drawbacks laid out for easier comprehension. Finally, to broaden the scope of optimization in the field of EM design and modeling, some prospective trends are pointed out to shed light on emerging research hotspots.

The Design Structure Matrix (DSM) is an established method used in dependency modelling, especially in the design of complex engineering systems. The generation of DSM is traditionally carried out through manual means and can involve interviewing experts to elicit critical system elements and the relationships between them. Such manual approaches can be time-consuming and costly. This paper presents a workflow that uses a Large Language Model (LLM) to support the generation of DSM and improve productivity. A prototype of the workflow was developed in this work and applied on a diesel engine DSM published previously. It was found that the prototype could reproduce 357 out of 462 DSM entries published (i.e. 77.3%), suggesting that the work can aid DSM generation. A no-code version of the prototype is made available online to support future research.

The energy efficiency optimization of Florida's residential buildings is essential for the reduction of fossil fuel consumption and greenhouse gas emissions. This optimization requires designers to accurately calculate the building energy loads at the design stage to efficiently design the cooling and heating systems. However, due to time and cost constraints, designers usually explore only a few design alternatives and simulate their energy use. This study developed an artificial-neural-network (ANN)-based generative design (GD) framework to automate the design process of detached residences while optimizing their energy performance. The ANN model was developed using the machine learning platform TensorFlow and some Python-based Keras libraries based on a big data set of about 17,000 newly constructed detached residences in Florida between the years 2009 and 2021. The GD framework was established using Autodesk Dynamo and the Autodesk Revit GD add-on that uses the nondominated sorting genetic algorithm (NSGA-II), in which multiple design parameters mainly relating to the house geometry and its energy performance were incorporated. Considering 10 independent variables, including the total wall area, roof area, floor area, and the windows' U-value, the ANN model predicted the required capacities of the cooling and heating systems in detached houses, with R2 values of 0.955 and 0.904, respectively. The ANN then was integrated within the GD framework for performance evaluation purposes. The findings of this study resulted in a fully automated 3-min design process of an energy-efficient detached house envelope, maximizing the productivity of designers and developers and greatly reducing the financial strain and time consumed using traditional techniques. [ABSTRACT FROM AUTHOR]

In this paper, a novel synthesis methodology for LNA impedance matching is proposed, which consists of two main phases: database generation and parameter optimization. In the former phase, a large‐scale sweep of electromagnetic simulations of passive components is performed to speed up parameter optimization. In the latter phase, the coordinate descent method is used as the optimizer of this methodology. Since frequently dozens of parameters need to be optimized in synthesize LNAs, the coordinate descent method can temporarily fix some parameters in each iteration, thus convert the optimization problem into multiple simple low or even one‐dimensional optimizations, which can eventually obtain high‐quality LNAs quickly in fewer descent iterations. This methodology is applied to a Ka‐band two‐stage differential LNA design using 65 nm CMOS technology, and the synthesis time is only 3 min, which is tens of times faster compared to existing synthesis methods. The proposed LNA circuit is fabricated and the measured results verify the feasibility of the synthesis method. [ABSTRACT FROM AUTHOR]

In the quest to enhance the efficiency of gas turbines, there is a growing demand for innovative solutions to optimize high-pressure turbine blade cooling. However, the traditional methods for achieving this optimization are known for their complexity and time-consuming nature. We present an automation framework to streamline the design, meshing, and structural analysis of cooling channels, achieving design automation at both the morphological and topological levels. This framework offers a comprehensive approach for evaluating turbine blade lifetime and enabling multidisciplinary design analyses, emphasizing flexibility in turbine cooling design through high-level CAD templates and knowledge-based engineering. The streamlined automation process, supported by a knowledge base, ensures continuity in both the mesh and structural simulation automations, contributing significantly to advancements in gas turbine technology. [ABSTRACT FROM AUTHOR]

The study aims to develop and assess an algorithm for efficiently generating parking spot layouts within predefined area outlines. The algorithm is an attempt to streamline the decision-making process by producing different design variants and optimizing the utilization of available space. The algorithm's primary objective is to streamline decision-making by generating diverse design variants while optimizing the use of available space, with a distinct focus on mitigating environmental impact and fostering ecological well-being. Researchers conduct thorough tests on the algorithm across various outlines, resulting in multiple layout options for each scenario. They analyzed five representative parking locations and compare the algorithm's results with the existing parking spot layouts. Throughout the evaluation process, they consider quantitative and qualitative data, considering the complexities of communication solutions within each context. The study findings indicate that the algorithm demonstrates comparable or superior performance to existing solutions. Overall, the study highlights the promising potential of algorithmic design approaches in the context of parking lot automated design. Achieving a balance between innovative designs and user-friendly layouts is crucial, and this is achievable by conducting comprehensive analyses that consider various factors. The consistent findings underscore the algorithm's potential to significantly contribute to sustainable design practices in parking lot layouts, highlighting decreased environmental strain, efficient land use, and creating urban spaces that prioritize ecological benefits. Furthermore, seamlessly integrating algorithmic solutions with existing communication systems is paramount to ensure practical applicability in real-world scenarios. This integration will enable more effective and practical implementation of the algorithm's outputs in actual parking lot design projects. [ABSTRACT FROM AUTHOR]

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Introduction. The current practice of pile foundation selection is a time-consuming, incoherent and non-standardized process. The aim of the study is to develop a methodology for optimizing structural calculations on the example of a pile field, based on cone penetration test data. For this purpose it is necessary: to prepare an algorithm for processing data from engineering-geological surveys; to develop a strictly deterministic process of justification of the best option depending on the cost of the pile foundation; to obtain a visual representation of the data for the possibility of verification of the selected solution.Materials and methods. A genetic algorithm is used to optimize structural calculations of the pile field, which is implemented using the Galapagos plug-in based on the Grasshopper visual programming language. Python programming language is used to prepare initial data of geotechnical engineering surveys.Results. Linked algorithms for cone penetration test data processing and preliminary estimation of the optimal pile foundation configuration based on its total cost, on the bearing capacity of the pile foundation soil were developed.Conclusions. The developed algorithms can be used for preliminary calculation and rapid evaluation of pile foundation options. The required input data can be generated from calculation programmes. Alternatively, selection and optimization can be performed directly in Python code, using Grasshopper and Rhino only for force extraction and subsequent visualization of the results. Areas for further research and development include: consideration of layered geotechnical elements; estimation of the bearing capacity of each foundation footing independently and according to the underlying geotechnical elements; grouping of piles according to their position in the pile field and loads; consideration of the non-linear behaviour of the soil mass.

Microfluidic mixers, a pivotal application of microfluidic technology, are primarily utilized for the rapid amalgamation of diverse samples within microscale devices. Given the intricacy of their design processes and the substantial expertise required from designers, the intelligent automation of microfluidic mixer design has garnered significant attention. This paper discusses an approach that integrates artificial neural networks (ANNs) with reinforcement learning techniques to automate the dimensional parameter design of microfluidic mixers. In this study, we selected two typical microfluidic mixer structures for testing and trained two neural network models, both highly precise and cost-efficient, as alternatives to traditional, time-consuming finite-element simulations using up to 10,000 sets of COMSOL simulation data. By defining effective state evaluation functions for the reinforcement learning agents, we utilized the trained agents to successfully validate the automated design of dimensional parameters for these mixer structures. The tests demonstrated that the first mixer model could be automatically optimized in just 0.129 s, and the second in 0.169 s, significantly reducing the time compared to manual design. The simulation results validated the potential of reinforcement learning techniques in the automated design of microfluidic mixers, offering a new solution in this field.