Seminar "Fundamentals of Data Analysis, Bayesian Optimization, and Experimental Design..."

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last updated：Jan 18, 2023

トリケップス

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Explaining various applications such as molecular and material design, anomaly detection and diagnosis in processes, and state estimation of plants.

Trikepps Co., Ltd. will hold a seminar titled "Fundamentals of Data Analysis and Bayesian Optimization and Their Application to Material, Process, and Equipment Design through Experimental Design Methods: Latest Cases" on March 20, 2023 (Monday) via Zoom. In this seminar, we will explain such (adaptive) experimental design methods, the data analysis theories necessary to implement experimental design methods, and the fundamentals of Bayesian optimization. We will also introduce specific examples of material design as well as process and equipment design, along with the latest research cases. We sincerely look forward to your participation. [Overview] ■ Date and Time: March 20, 2023 (Monday) 10:30 AM - 4:30 PM ■ Method: Zoom ■ Participation Fee - For individual participation: 51,700 yen [including tax] - For group application: 62,700 yen [including tax] (up to 3 participants) *For more details, please refer to the PDF document or feel free to contact us.

Related Link - http://www.triceps.co.jp/seminar/s230320aw.html

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In the field of research and development, such as the development of high-performance materials and design through computer simulation, experimental design methods are utilized to efficiently conduct simulations, experiments, and manufacturing. For example, if there are five experimental parameters or simulation conditions (such as reactor volume, reaction temperature, reaction time, etc.), each with 10 candidate values, the total number of combinations would reach 100,000. However, by using experimental design methods, it is possible to model the relationships between experimental parameters, device performance, material properties, and activity using statistical methods from a few selected simulation or experimental candidates, thereby efficiently exploring candidates to achieve the desired device performance, properties, and activity. By leveraging simulation and experimental data in this way, efficient material design and process and device design can be achieved.