scholarly journals MLGen: Generative Design Framework Based on Machine Learning and Topology Optimization

2021 ◽  
Vol 11 (24) ◽  
pp. 12044
Author(s):  
Nikos Ath. Kallioras ◽  
Nikos D. Lagaros
Keyword(s):  
Machine Learning ◽  
Topology Optimization ◽  
Architectural Design ◽  
Parametric Design ◽  
Optimization Problems ◽  
Design Procedure ◽  
Performance Criteria ◽  
Design Framework ◽  
Generative Design ◽  
New Era

Design and manufacturing processes are entering into a new era as novel methods and techniques are constantly introduced. Currently, 3D printing is already established in the production processes of several industries while more are continuously being added. At the same time, topology optimization has become part of the design procedure of various industries, such as automotive and aeronautical. Parametric design has been gaining ground in the architectural design literature in the past years. Generative design is introduced as the contemporary design process that relies on the utilization of algorithms for creating several forms that respect structural and architectural constraints imposed, among others, by the design codes and/or as defined by the designer. In this study, a novel generative design framework labeled as MLGen is presented. MLGen integrates machine learning into the generative design practice. MLGen is able to generate multiple optimized solutions which vary in shape but are equivalent in terms of performance criteria. The output of the proposed framework is exported in a format that can be handled by 3D printers. The ability of MLGen to efficiently handle different problems is validated via testing on several benchmark topology optimization problems frequently employed in the literature.

scholarly journals Harmony Search-Based Approach for Multi-Objective Software Architecture Reconstruction

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1906
Author(s):  
Amarjeet Prajapati ◽  
Zong Woo Geem
Keyword(s):  
Evolutionary Algorithms ◽  
Software Architecture ◽  
Architectural Design ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Performance Criteria ◽  
Software System ◽  
Multi Objective

The success of any software system highly depends on the quality of architectural design. It has been observed that over time, the quality of software architectural design gets degraded. The software system with poor architecture design is difficult to understand and maintain. To improve the architecture of a software system, multiple design goals or objectives (often conflicting) need to be optimized simultaneously. To address such types of multi-objective optimization problems a variety of metaheuristic-oriented computational intelligence algorithms have been proposed. In existing approaches, harmony search (HS) algorithm has been demonstrated as an effective approach for numerous types of complex optimization problems. Despite the successful application of the HS algorithm on different non-software engineering optimization problems, it gained little attention in the direction of architecture reconstruction problem. In this study, we customize the original HS algorithm and propose a multi-objective harmony search algorithm for software architecture reconstruction (MoHS-SAR). To demonstrate the effectiveness of the MoHS-SAR, it has been tested on seven object-oriented software projects and compared with the existing related multi-objective evolutionary algorithms in terms of different software architecture quality metrics and metaheuristic performance criteria. The experimental results show that the MoHS-SAR performs better compared to the other related multi-objective evolutionary algorithms.

scholarly journals Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers

2021 ◽  
Vol 11 (12) ◽  
pp. 5641
Author(s):  
Chiu-Yu Yeh ◽  
Yaw-Shyan Tsay
Keyword(s):  
Machine Learning ◽  
Functional Activity ◽  
Architectural Design ◽  
Parametric Design ◽  
Simulation Software ◽  
Evaluation Tool ◽  
Accurate Analysis ◽  
Interior Decoration ◽  
Estimation Formula ◽  
Almost All

In Taiwan, activity centers such as school auditoriums and gymnasiums are common multi-functional spaces that are often used for performances, singing, and speeches. However, most cases are designed using only Sabine’s equation for architectural acoustics. Although that estimation formula is simple and fast, the calculation process ignores many details. Furthermore, while more accurate analysis can be obtained through acoustics simulation software, it is more complicated and time-consuming and thus is rarely used in practical design. The purpose of this study is to use machine learning to propose a predictive model of acoustic indicators as a simple evaluation tool for the architectural design and interior decoration of multi-functional activity centers. We generated 800 spaces using parametric design, adopting Odeon to obtain acoustic indicators. The machine learning model was trained with basic information of the space. We found that through GBDT and ANN algorithms, almost all acoustic indicators could be predicted within JND ± 2, and the JND of C50, C80, STI, and the distribution of SPL could reach within ±1. Through machine learning methods, we established a convenient, fast, and accurate prediction model and were able to obtain various acoustic indicators of the space without 3D-modeling or simulation software.

scholarly journals Cascaded Metasurface Design Using Electromagnetic Inversion with Gradient-Based Optimization

2020 ◽  
Author(s):  
Trevor Brown ◽  
Puyan Mojabi
Keyword(s):  
Optimization Problems ◽  
Design Procedure ◽  
Field Performance ◽  
Optimization Procedure ◽  
Gradient Algorithm ◽  
Performance Criteria ◽  
Inversion Algorithm ◽  
Interior Space ◽  
Output Field ◽  
Cost Functionals

This paper presents an electromagnetic inversion algorithm for the design of cascaded metasurfaces that enables the design process to begin from more practical output field specifications such as a desired power pattern or far-field performance criteria. Thus, this method combines the greater field transformation support of multiple metasurfaces with the flexibility of the electromagnetic inverse source framework. To this end, two optimization problems are formed: one associated with the interior space between two metasurfaces, and the other for the exterior space. The cost functionals corresponding to each of these two optimization problems are minimized using the nonlinear conjugate gradient algorithm with analytic expressions for the gradient operators. A total variation regularizer is incorporated into the optimization procedure to favour smooth field variations from one unit cell to the next. The numerical implementation of the developed design procedure is presented in detail along with several two-dimensional (2D) simulated examples to demonstrate the capabilities of the method.

scholarly journals Generative design: information flow between genetic algorithm and parametric design in a steel structure construction

2021 ◽  
Vol 21 (4) ◽  
pp. 271-289
Author(s):  
Renato Godoi da Cruz ◽  
Cláudia Maria Arcipreste ◽  
Rafael Lemieszek Pinheiro ◽  
Rovadávia Aline de Jesus Ribas
Keyword(s):  
Genetic Algorithms ◽  
Design Process ◽  
Information Flow ◽  
Architectural Design ◽  
Parametric Design ◽  
Design Method ◽  
Steel Structure ◽  
Parametric Modelling ◽  
Generative Design ◽  
Design Options

Abstract This paper describes the construction of an information flow that combines parametric modelling and genetic algorithms to optimise both the total weight of a structured steel roof and the shape of its surface to maximise the generation potential of photovoltaic energy. Through the systematisation of a design process using genetic algorithms integrated with generative design as an architectural design method, architects can work with problem parameterisation, going one step further than the mere formal proposition of solutions.As a result, we present a process in which there is a dialogical relationship between the architect and project in a continuous flow of information in the initial steps of the design process, guided by predefined optimisation objectives. Therefore, it is relevant to note that the designer accepts a reduced creative control over the final shape in favor of control over the core principles that constitute the optimisation algorithms. An approach that involved the designer in a process of post computational processing evaluations is also highlighted, giving him better support to discuss and analyze the design options.

Advantages of surrogate models for architectural design optimization

2015 ◽  
Vol 29 (4) ◽  
pp. 471-481 ◽  
Author(s):  
Thomas Wortmann ◽  
Alberto Costa ◽  
Giacomo Nannicini ◽  
Thomas Schroepfer
Keyword(s):  
Genetic Algorithms ◽  
Design Problem ◽  
Architectural Design ◽  
Optimization Problems ◽  
Resource Depletion ◽  
Surrogate Models ◽  
Performance Criteria ◽  
Design Parameters ◽  
Metaheuristic Methods ◽  
The Impact

AbstractClimate change, resource depletion, and worldwide urbanization feed the demand for more energy and resource-efficient buildings. Increasingly, architectural designers and consultants analyze building designs with easy-to-use simulation tools. To identify design alternatives with good performance, designers often turn to optimization methods. Randomized, metaheuristic methods such as genetic algorithms are popular in the architectural design field. However, are metaheuristics the best approach for architectural design problems that often are complex and ill defined? Metaheuristics may find solutions for well-defined problems, but they do not contribute to a better understanding of a complex design problem. This paper proposes surrogate-based optimization as a method that promotes understanding of the design problem. The surrogate method interpolates a mathematical model from data that relate design parameters to performance criteria. Designers can interact with this model to explore the approximate impact of changing design variables. We apply the radial basis function method, a specific type of surrogate model, to two architectural daylight optimization problems. These case studies, along with results from computational experiments, serve to discuss several advantages of surrogate models. First, surrogate models not only propose good solutions but also allow designers to address issues outside of the formulation of the optimization problem. Instead of accepting a solution presented by the optimization process, designers can improve their understanding of the design problem by interacting with the model. Second, a related advantage is that designers can quickly construct surrogate models from existing simulation results and other knowledge they might possess about the design problem. Designers can thus explore the impact of different evaluation criteria by constructing several models from the same set of data. They also can create models from approximate data and later refine them with more precise simulations. Third, surrogate-based methods typically find global optima orders of magnitude faster than genetic algorithms, especially when the evaluation of design variants requires time-intensive simulations.

Machine Learning to Aid Tuning of Numerical Parameters in Topology Optimization

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Matthew E. Lynch ◽  
Soumalya Sarkar ◽  
Kurt Maute
Keyword(s):  
Machine Learning ◽  
Topology Optimization ◽  
Optimization Problems ◽  
Bayesian Optimization ◽  
Computational Time ◽  
Optimization Approach ◽  
Learning Approaches ◽  
Solution Quality ◽  
Topology Optimization Problem ◽  
Tuning Parameters

Abstract Recent advances in design optimization have significant potential to improve the function of mechanical components and systems. Coupled with additive manufacturing, topology optimization is one category of numerical methods used to produce algorithmically generated optimized designs making a difference in the mechanical design of hardware currently being introduced to the market. Unfortunately, many of these algorithms require extensive manual setup and control, particularly of tuning parameters that control algorithmic function and convergence. This paper introduces a framework based on machine learning approaches to recommend tuning parameters to a user in order to avoid costly trial and error involved in manual tuning. The algorithm reads tuning parameters from a repository of prior, similar problems adjudged using a dissimilarity metric based on problem metadata and refines them for the current problem using a Bayesian optimization approach. The approach is demonstrated for a simple topology optimization problem with the objective of achieving good topology optimization solution quality and then with the additional objective of finding an optimal “trade” between solution quality and required computational time. The goal is to reduce the total number of “wasted” tuning runs that would be required for purely manual tuning. With more development, the framework may ultimately be useful on an enterprise level for analysis and optimization problems—topology optimization is one example but the framework is also applicable to other optimization problems such as shape and sizing and in high-fidelity physics-based analysis models—and enable these types of advanced approaches to be used more efficiently.

scholarly journals Cascaded Metasurface Design Using Electromagnetic Inversion with Gradient-Based Optimization

2020 ◽  
Author(s):  
Trevor Brown ◽  
Puyan Mojabi
Keyword(s):  
Optimization Problems ◽  
Design Procedure ◽  
Field Performance ◽  
Optimization Procedure ◽  
Gradient Algorithm ◽  
Performance Criteria ◽  
Inversion Algorithm ◽  
Interior Space ◽  
Output Field ◽  
Cost Functionals

This paper presents an electromagnetic inversion algorithm for the design of cascaded metasurfaces that enables the design process to begin from more practical output field specifications such as a desired power pattern or far-field performance criteria. Thus, this method combines the greater field transformation support of multiple metasurfaces with the flexibility of the electromagnetic inverse source framework. To this end, two optimization problems are formed: one associated with the interior space between two metasurfaces, and the other for the exterior space. The cost functionals corresponding to each of these two optimization problems are minimized using the nonlinear conjugate gradient algorithm with analytic expressions for the gradient operators. A total variation regularizer is incorporated into the optimization procedure to favour smooth field variations from one unit cell to the next. The numerical implementation of the developed design procedure is presented in detail along with several two-dimensional (2D) simulated examples to demonstrate the capabilities of the method.

scholarly journals How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Oliwia Koteluk ◽  
Adrian Wartecki ◽  
Sylwia Mazurek ◽  
Iga Kołodziejczak ◽  
Andrzej Mackiewicz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Medical Data ◽  
General Process ◽  
New Era ◽  
Automated Evaluation ◽  
Machine Learning Applications ◽  
And Training ◽  
Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

scholarly journals Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1055
Author(s):  
Qian Sun ◽  
William Ampomah ◽  
Junyu You ◽  
Martha Cather ◽  
Robert Balch
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
History Matching ◽  
Optimization Problems ◽  
Learning Technologies ◽  
Petroleum Engineering ◽  
Support Vector ◽  
Learning Approaches ◽  
Field Development ◽  
Proxy Models

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

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