Bayesian optimizationParameter identificationIn this work, we advocate using Bayesian techniques for inversely identifying material parameters for multiscale crystal plasticity models. Multiscale approaches for modeling polycrystalline materials may significantly reduce the effort necessary for characterizing such ...
We explore the inverse rendering problem of procedural material parameter estimation from photographs, presenting a unified view of the problem in a Bayesian framework. In addition to computing point estimates of the parameters by optimization, our framework uses a Markov Chain Monte Carlo approach to...
According to the mathematical statistics theory and Bayesian technique, a method for data processing and optimization in geotechnical engineering is put forward to solve the problem caused by model uncertainty due to lack of enough accurate field data. Meanwhile, the data of bearing capacity of piles...
We conclude with a discussion of Bayesian optimization software and future research directions in the field. Within our tutorial material we provide a generalization of expected improvement to noisy evaluations, beyond the noise-free setting where it is more commonly applied. This generalization is ...
Its primary function is to guide the optimization algorithm in selecting the next point x in the hyperparameter space to evaluate. The acquisition function quantifies the expected utility or improvement of evaluating the objective function at a particular point x, based on the existing data and ...
Bayesian optimization (BO) is an indispensable tool to optimize objective functions that either do not have known functional forms or are expensive to evaluate. Currently, optimal experimental design is always conducted within the workflow of BO leading
Hyperparameter optimization (HPO) of deep neural networks plays an important role of performance and efficiency of detection networks. Especially for cloud computing, automatic HPO can greatly reduce the network deployment cost by taking advantage of the computing power. Benefiting from its global-optima...
Bayesian optimization relies on acquisition functions to provide the candidate parameter points that navigate the underlying model space. Acquisition functions define a strategy to manage the trade-off between exploring the parameter space and exploiting regions that yielded improvement for previous samples ...
Bayesian optimization for hyperparameter modifications leads to an even greater increase in model performance. The operation of every component is explained in depth in this part, together with the pertinent equations [25]. The suggested hybrid model ProtICNN-BiLSTM is architecturally illustrated in ...
Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of...