Browsing by Subject "Gaussian process regression"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Multi-Objective Bayesian Optimization Supported by Gaussian Process Classifiers and Conditional Probabilities
    (ASME, 2022-11-11) Valladares, Homero; Tovar, Andres; Mechanical Engineering, School of Engineering and Technology
    In the last years, there has been an increasing effort to develop Bayesian methodologies to solve multi-objective optimization problems. Most of these methods can be classified in two groups: infilling criterion-based methods and aggregation-based methods. The first group employs an index that quantifies the gain that a new design can produce in the current Pareto front while the last group uses a (possibly non-linear) aggregation function and a weighting vector to identify a Pareto design. Most infilling-based methods have been developed to solve two-objective optimization problems. Aggregation-based methods enable the solution of many-objective optimization problems but their performance depends on the set of weighting vectors, which are often selected randomly. This study proposes a novel multi-objective Bayesian framework that exploits the rich probabilistic information that can be extracted from Gaussian process (GP) classifiers and the ability of conditional probabilities to capture design preferences. In the proposed framework, a GP classifier is trained to identify design zones that potentially contain Pareto designs. The training process involves the inference of a latent GP that encodes input-space interactions that describe a Pareto design. This latent GP enables the solution of many-objective optimization problems with any standard acquisition function and without the prescription of a weighting vector. Conditional probabilities are utilized to define design goals that promote a uniform expansion of the Pareto front. The proposed approach is demonstrated with two benchmark analytical problems and the design optimization of sandwich composite armors for blast mitigation, which involves expensive finite element simulations.
  • Thumbnail Image
    Item
    Training Machine Learning Potentials for Reactive Systems: A Colab Tutorial on Basic Models
    (Wiley, 2024) Pan, Xiaoliang; Snyder, Ryan; Wang, Jia-Ning; Lander, Chance; Wickizer, Carly; Van, Richard; Chesney, Andrew; Xue, Yuanfei; Mao, Yuezhi; Mei, Ye; Pu, Jingzhi; Shao, Yihan; Chemistry and Chemical Biology, School of Science
    In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field - the training of system-specific MLPs for reactive systems - with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self-guided Colab tutorial (https://cc-ats.github.io/mlp_tutorial/), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple feedforward neural network (FNN) and kernel-based (using Gaussian process regression, GPR) models by fitting the two-dimensional Müller-Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot-SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energies and forces for the molecular configurations in a Claisen rearrangement reaction.