arXiv:2112.06769
Multi-objective simulation optimization of the adhesive bonding process of materials
원문 초록
Automotive companies are increasingly looking for ways to make their products lighter, using novel materials and novel bonding processes to join these materials together. Finding the optimal process parameters for such adhesive bonding process is challenging. In this research, we successfully applied Bayesian optimization using Gaussian Process Regression and Logistic Regression, to efficiently (i.e., requiring few experiments) guide the design of experiments to the Pareto-optimal process parameter settings.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Distributional encoding for Gaussian process regression with qualitative inputs
LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization
Active Learning for Manifold Gaussian Process Regression
Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control
Vector Contrastive Learning For Pixel-Wise Pretraining In Medical Vision
GRASP: Grouped Regression with Adaptive Shrinkage Priors
토론
이 논문과 연결된 포럼 글이 아직 없습니다.