Impact: Design With All Senses 2019
DOI: 10.1007/978-3-030-29829-6_2
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An Interactive Structural Optimization of Space Frame Structures Using Machine Learning

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Cited by 10 publications
(4 citation statements)
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“…Indeed, the application of artificial intelligence and machine learning (AI/ML) to molecular simulations has increased in popularity recently due to its ability to efficiently model complex functions in data-rich domains. There have been a number of demonstrations from domain scientists for specific challenges such as reaction network elucidation, thermochemistry prediction, structure optimization, accelerating individual calculations, and integration with characterization (see recent reviews for a more thorough discussion , ). Most of these tasks are variations on the same fundamental problem: modeling heterogeneous catalysis.…”
Section: Introductionmentioning
confidence: 99%
“…Indeed, the application of artificial intelligence and machine learning (AI/ML) to molecular simulations has increased in popularity recently due to its ability to efficiently model complex functions in data-rich domains. There have been a number of demonstrations from domain scientists for specific challenges such as reaction network elucidation, thermochemistry prediction, structure optimization, accelerating individual calculations, and integration with characterization (see recent reviews for a more thorough discussion , ). Most of these tasks are variations on the same fundamental problem: modeling heterogeneous catalysis.…”
Section: Introductionmentioning
confidence: 99%
“…Offering one substantial difference from conventional iterative processes for searching for potential solutions. Because solutions emerge from local rules, exploring new outcomes relies on the generation of new global results [11].…”
Section: Artificial Intelligence For Materials Predictionmentioning
confidence: 99%
“…Furthermore, neural networks can be used to solve the computational problem in the field of structural optimization (Aksöz and Preisinger 2019). The authors use an artificial neural network (ANN) to learn the stress conditions and coping methods in finite element analysis, then use the generated sample data to train the neural network, and finally apply the trained neural network to generate structure solutions based on the stress conditions given by the user.…”
Section: Machine Learningmentioning
confidence: 99%