AI-based Design Research Articles

Quotient Complex (QC)-Based Machine Learning for 2D Hybrid Perovskite Design.

With remarkable stability and exceptional optoelectronic properties, two-dimensional (2D) halide layered perovskites hold immense promise for revolutionizing photovoltaic technology. Effective data representations are key to the success of all learning models. Currently, the lack of comprehensive and accurate material representations has hindered AI-based design and discovery of 2D perovskites, limiting their potential for advanced photovoltaic applications. In this context, this work introduces a novel computational topology framework termed the quotient complex (QC), which serves as the foundation for the material representation. The proposed QC-based features are seamlessly integrated with learning models for the advancement of 2D perovskite design. At the heart of this framework lies the quotient complex descriptors (QCDs), representing a quotient variation of simplicial complexes derived from materials' unit cell and periodic boundary conditions. Differing from prior material representations, this approach encodes higher-order interactions and periodicity information simultaneously. Based on the well-established new materials for solar energetics (NMSE) databank, the proposed QC-based machine learning models exhibit superior performance against all existing counterparts. This underscores the paramount role of periodicity information in predicting material functionality, while also showcasing the remarkable efficiency of the QC-based model in characterizing materials' structural attributes.

Is Direct Sequence Spread Spectrum Modulation Promising for AI-Based Design of 6G Networks?

Is Direct Sequence Spread Spectrum Modulation Promising for AI-Based Design of 6G Networks?

The Role of Artificial Intelligence in Fashion Design

This research investigates the role of artificial intelligence (AI) in fashion design, focusing on its potential to foster innovative and aesthetically striking designs. The study explores the level of interest among emerging fashion designers, including students from design and arts colleges, as well as individuals keen on distinctive and avant-garde fashion. The research aims to achieve its objectives using descriptive, analytical, and quasi-experimental methodologies. Findings reveal that while there is significant interest among fashion design students and professionals in AI-based design, challenges arise from the limited availability of learning resources, hindering their ability to fully grasp and apply this technology. The study concludes that there is an urgent need to integrate AI into fashion design education, as it can significantly benefit the industry and its practitioners. Recommendations include expanding specialised research connecting AI with fashion design, enhancing university curricula with crucial AI concepts, and focusing on technological advancements and AI applications in fashion design and forecasting.

Filling the simulation-to-reality gap: high-degree-of-freedom AI-optimized photonic crystal nanobeam resonators with fabrication tolerance.

In this work, we unveil a novel, to the best of our knowledge, AI-based design method (AIDN1) specifically developed for photonic crystal resonator designs, capable of handling complex designs with over 10 degrees of freedom (DoFs) and considering practical fabrication uncertainties to minimize the common simulation-to-reality (sim2real) gap. Especially, we introduce an ultrashort (<5 µm) curved nanobeam resonator, which obtains an ultrahigh theoretical quality factor (Q-factor) of 2 × 107 and maintains a theoretical Q-factor above 105 even under high fabrication variations. Importantly, we emphasize that AIDN1 is generalizable and our work serves as a solid foundation for future laser fabrication endeavors beyond the realm of ultrashort 1D photonic crystal (PhC) resonators.

AI-Based Design With Data Trimming for Hybrid Phase Shift Modulation for Minimum-Current-Stress Dual Active Bridge Converter

Dual active bridge (DAB) topology has been recognized as the key circuit for the next generation of high-frequency-link power conversion systems. In order to realize desired operating performance, DAB modulation strategies need to be selected carefully. Different modulation strategies have been considered and combined into a hybrid one, which is able to fully optimize performance. However, to develop a hybrid modulation strategy, the conventional methods including harmonic model and piecewise model have difficulty in balancing modeling accuracy and manpower burden. Although recent data-driven modulation approaches can automate the design process, the accuracy of data-driven models decreases nontrivially with the existence of outliers and without sufficient data. Hence, to alleviate human-dependence and improve modeling accuracy, this paper proposes an AI-based design with data trimming (AI-DT) for hybrid phase shift modulation. Two two-degree-of-freedom modulation strategies are considered for the sake of optimal current stress performance. AI-DT firstly adopts the one-class support vector machine (SVM) to exclude outliers. Second, the state-of-the-art extreme gradient boosting (XGBoost), which is insensitive to training data size, is adopted to build data-driven current stress models for the DAB converter. After that, differential evolution (DE) algorithm helps to choose modulation strategy and optimize modulation parameters for optimal current stress. Generally, the proposed AI-DT is developed in an automated fashion, which largely relieves manual computational complexity while exhibiting satisfactory modulation accuracy. The effectiveness of the proposed AI-DT approach has been experimentally verified with a 1kW hardware prototype, realizing optimal current stress under full operating conditions with more than 96.5% as peak efficiency.

Conceptual Design Algorithm Configuration Using Generative Design Techniques

Purpose: The aim of this study is to investigate design automation and generative design cases in the field of construction using artificial intelligence (AI) and generate alternatives using generative design techniques in the planning and early design stages of a building. Previous studies indicate that AI-based design automation techniques can be used to design the paradigm of the existing construction and design field, thus improving the construction productivity. In this study, we have conducted additional research to understand the application of generative design in the planning and initial design stages of buildings to provide research directions for Korean design automation and generative design suitable for domestic situations. Method: We have used Autodesk

Efficient Design Method for a Forward-Converter Transformer Based on a KNN–GRU–DNN Model

This letter proposes an efficient design method for a forward-converter transformer (FCT) with artificial intelligence (AI). Conventional FCT design is inefficient because it requires numerous repeated design processes. To solve this problem, this letter proposes FCT design by applying a K-nearest neighbors, gated recurrent unit, and deep neural network model. The design estimation accuracy of the proposed AI model was over 91% based on Google colaboratory validation. The proposed transformer design also satisfied the design requirements with less than 1,450 epochs. Once the learning process is completed, the proposed AI-based transformer design can obtain various FCT designs without further repeated training procedures. To verify the proposed design results, this letter conducted finite-element method (FEM) simulations using ANSYS Electronics Desktop 2018.2 and hardware-in-the-loop (HIL) experiments using OPAL-RT with the transformer design values resulting from the AI-based design model. According to the FEM simulations and HIL experiments, it is verified that the secondary winding induced voltage of the transformer designed by the AI-based model satisfies the design requirements.

Mapping artificial intelligence-based methods to engineering design stages: a focused literature review

Abstract Engineering design has proven to be a rich context for applying artificial intelligence (AI) methods, but a categorization of such methods applied in AI-based design research works seems to be lacking. This paper presents a focused literature review of AI-based methods mapped to the different stages of the engineering design process and describes how these methods assist the design process. We surveyed 108 AI-based engineering design papers from peer-reviewed journals and conference proceedings and mapped their contribution to five stages of the engineering design process. We categorized seven AI-based methods in our dataset. Our literature study indicated that most AI-based design research works are targeted at the conceptual and preliminary design stages. Given the open-ended, ambiguous nature of these early stages, these results are unexpected. We conjecture that this is likely a result of several factors, including the iterative nature of design tasks in these stages, the availability of open design data repositories, and the inclination to use AI for processing computationally intensive tasks, like those in these stages. Our study also indicated that these methods support designers by synthesizing and/or analyzing design data, concepts, and models in the design stages. This literature review aims to provide readers with an informative mapping of different AI tools to engineering design stages and to potentially motivate engineers, design researchers, and students to understand the current state-of-the-art and identify opportunities for applying AI applications in engineering design.

Artificial intelligence-designer for high-rise building sketches with user preferences

Architectural design is a highly intellectual creative work. It is so interesting and fascinating to explore artificial intelligence (AI)-based architectural design methodology, i.e. exploring the creativity of machines from a design perspective. Conceptual design, which gives a sketch of a building satisfying certain constraints, requires very high creativity and is an important step in the architectural design process. This study proposes a novel AI-based design method of high-rise building sketches with user preferences (termed AI-designer), where AI-designer can control the sketch generation in two aspects: shape preferences and architectural fusion. To achieve these two functions, the AI-based design method contains the shape preference module with Self-Sparse GAN and architecture fusion module. The proposed shape preference module with Self-Sparse GAN enables the user to specify the shape of the building sketch by a simple hand-drawn sketch, where the Self-Similarity score (SS-score) is proposed to obtain the degree of similarity of shapes for selecting a specified number of sketches generated by the Self-Sparse GAN. Meanwhile, the architecture fusion module allows to implement user-preference elements (such as artistic style and local culture) with the generated sketches for further forming a clearer architectural fusion sketch by using the line loss with a feature-based attention mechanism. The design results show that the proposed AI-designer can not only generate a large number of desired architectural sketches with a specific theme while remaining diversity in just a few seconds, but also form clearer sketches with architectural fusion by re-rendering using user-specified elements.

AI-Based Design of Hybrid Ionic Polymer–Metal Composite with CNT and Graphene

AI-Based Design of Hybrid Ionic Polymer–Metal Composite with CNT and Graphene

Cross-Adversarial Learning for Molecular Generation in Drug Design.

Molecular generation is an important but challenging task in drug design, as it requires optimization of chemical compound structures as well as many complex properties. Most of the existing methods use deep learning models to generate molecular representations. However, these methods are faced with the problems of generation validity and semantic information of labels. Considering these challenges, we propose a cross-adversarial learning method for molecular generation, CRAG for short, which integrates both the facticity of VAE-based methods and the diversity of GAN-based methods to further exploit the complex properties of Molecules. To be specific, an adversarially regularized encoder-decoder is used to transform molecules from simplified molecular input linear entry specification (SMILES) into discrete variables. Then, the discrete variables are trained to predict property and generate adversarial samples through projected gradient descent with corresponding labels. Our CRAG is trained using an adversarial pattern. Extensive experiments on two widely used benchmarks have demonstrated the effectiveness of our proposed method on a wide spectrum of metrics. We also utilize a novel metric named Novel/Sample to measure the overall generation effectiveness of models. Therefore, CRAG is promising for AI-based molecular design in various chemical applications.

Optimizing reinforced concrete beams cost based on AI-based Lagrange functions

ABSTRACT This study aims to develop artificial neural networks (ANNs)-based forward Lagrange networks optimizing ductile doubly reinforced concrete (RC) beams. Cost (CIb ) of materials and manufacture were established as an objective function, which is minimized considering constraints according to engineer’s needs. Large datasets of 100,000 were used to derive an AI-based objective function for cost (CIb , minimizing cost of an RC beams) as a function of forward input parameters, replacing complex analytical objective functions. A resilient design capable of finding concrete beams beyond human efficiency is based on equality and inequality constraints, which are implanted in AI-based Lagrange. Optimal designs are not simple especially when multiple constraining conditions are to be considered. Neither is it possible for engineers to pre-assign constraining conditions which can be sequentially calculated in an output of a conventional design. Cost of RC beams minimized by forward Lagrange networks was reduced 18%–26% compared to probable beam designs. AI-based design charts with eight forward outputs (ϕMn,Mu,Mcr,εrt_0.003,εrc_0.003,Δimme,Δlong,CIb ) based on nine forward inputs (L, h, b, fy,f’ c, ρrt,ρrc,MD,ML ) are proposed to assist engineers to design ductile doubly RC beams, presenting minimized CIb in the preliminary design stage.

Electromagnetic–AI-Based Design Optimization of SynRM Drives

Characterization modules in electric machines and drives design optimization environments typically involve the use of electromagnetic finite element-state space models that require large number of iterations and computational time. It is shown in this work that the utilization of a Taguchi orthogonal arrays method in conjunction with a particle swarm optimization (PSO), search algorithm in a design optimization case study of a synchronous reluctance motor (SynRM) drive, resulted in about 80% reduction of computational time.

AI-based design of a nuclear reactor core

The authors developed an artificial intelligence (AI)-based algorithm for the design and optimization of a nuclear reactor core based on a flexible geometry and demonstrated a 3× improvement in the selected performance metric: temperature peaking factor. The rapid development of advanced, and specifically, additive manufacturing (3-D printing) and its introduction into advanced nuclear core design through the Transformational Challenge Reactor program have presented the opportunity to explore the arbitrary geometry design of nuclear-heated structures. The primary challenge is that the arbitrary geometry design space is vast and requires the computational evaluation of many candidate designs, and the multiphysics simulation of nuclear systems is very time-intensive. Therefore, the authors developed a machine learning-based multiphysics emulator and evaluated thousands of candidate geometries on Summit, Oak Ridge National Laboratory’s leadership class supercomputer. The results presented in this work demonstrate temperature distribution smoothing in a nuclear reactor core through the manipulation of the geometry, which is traditionally achieved in light water reactors through variable assembly loading in the axial direction and fuel shuffling during refueling in the radial direction. The conclusions discuss the future implications for nuclear systems design with arbitrary geometry and the potential for AI-based autonomous design algorithms.

ARTIFICIAL INTELLIGENCE VS DESIGNER: THE IMPACT OF ARTIFICIAL INTELLIGENCE ON DESIGN PRACTICE

Technological advances, including the use of possibilities offered by artificial intelligence (AI), have become an area of strategic importance and a key driver of economic development. AI today has been integrated into a variety of economies, the design industry is no exception: AI is being increasingly applied in the development of design products and services. However, as technological breakthroughs rapidly shift the borders between the work tasks performed by humans and those performed by machines and algorithms, global labor markets are undergoing major transformations. This raises the question: how are these changes affecting and will continue to affect designers’ work in the future? What skill sets will be needed for designers to begin or continue working in the industry? The article aims to perform a meta-analysis, summarizing the research on the impact of AI on the designer’s professional activity and test the capabilities and results of AI-based design solutions. Research methods – theoretical – research and analysis of literature and Internet resources; empirical – case study to analyze possibilities and results of AI-based design solutions.

Effect of the rate of artificial insemination and paternity knowledge on the genetic gain for French meat sheep breeding programs

This study assessed the consequences of the reduced use of Artificial Insemination (AI) on genetic gain with constraints on inbreeding changes for a repeated maternal trait in meat sheep. Using a stochastic model, scenarios inspired by the French context were compared and defined by different strategies (no AI, AI without and with a progeny test of young rams), rate of AI (from 0 to 80%) and proportions of pedigreed females born from Natural Mating (NM). Additional gains from +15 to + 50% were obtained for the AI-based design without progeny testing, and from +57 to + 84% for the AI-based design with progeny testing compared to the no AI design. When only part of the females were from a known sire, genetic gain strongly decreased in no AI breeding programs, with a reduced effect when the rate of AI increased.

AI-based design of urban stormwater detention facilities accounting for carryover storage

Rapid urbanization in metropolitan areas easily triggers flashy floods. Urban drainage systems conveying stormwater out of cities are key infrastructure elements for flood mitigation. This study develops an intelligent urban flood drainage system accounting for carryover storage through optimizing the multi-objective operation rules of pumping stations for effectual flood management in Taipei City. The Yu-Cheng pumping station constitutes the study case, and a large number of datasets collected from 17 typhoon/storm events are adopted for model construction and validation. Three objective functions are designed to minimize: (1) the sum of water level fluctuations in the flood storage pond (FSP); (2) the sum of peak FSP water levels; and (3) the mean absolute difference of pump switches between two consecutive times along operation sequence. The non-dominated sorting genetic algorithm II (NSGA-II) is applied to searching the Pareto-optimal solutions that optimize the trade-off between the objectives. We next formulate the optimal operation rules through a two-tier sorting process based on a compromised Pareto-optimal solution. The comparison of the simulated results obtained from both the optimal operation rules and current operation rules indicate that the optimal operation rules outperform current operation rules for all three objectives, with improvement rates reaching 43% (OBJ1), 3% (OBJ2) and 71% (OBJ3), respectively. We demonstrate that the derived intelligent urban flood drainage system can serve as reliable and efficient operational strategies for urban flood management and flood risk mitigation.

패션산업에서 인공지능 기반의 디자인 프로세스 혁신에 관한 연구

패션산업에서 인공지능 기반의 디자인 프로세스 혁신에 관한 연구

Artificial intelligence for waste minimization in the process industry

One of the most effective means of minimizing the generation of wastes in the process industry is through process design/modification and process control. It is very difficult to resort to conventional algorithmic methods to design and control a process with waste minimization in mind when the available information pertaining to a process is imprecise, incomplete, and uncertain, and the accessible knowledge is in symbolic form. In contrast, artificial intelligence (AI) techniques are viable alternatives for dealing with the information and knowledge of these types. In the present work, the basic characteristics of waste minimization are described, and the applicability of AI techniques to waste minimization is delineated. Three AI-based design and control systems developed specifically for waste minimization are described.

082 AI-based design and simulation of hydroelectric power systems

082 AI-based design and simulation of hydroelectric power systems