Publications

Tran, Anh; Visintainer, Robert J.; Furlan, John M.; Pagalthivarthi, Krishnan V.; Garman, Mohamed G.; Cutright, Aaron C.; Wang, Yan W.

Abstract not provided.

Journal of Computing and Information Science in Engineering

Tran, Anh; Wildey, Tim; McCann, Scott

Bayesian optimization (BO) is an efiective surrogate-based method that has been widely used to optimize simulation-based applications. While the traditional Bayesian optimization approach only applies to single-fidelity models, many realistic applications provide multiple levels of fidelity with various computational complexity and predictive capability. In this work, we propose a multi-fidelity Bayesian optimization method for design applications with both known and unknown constraints. The proposed framework, called sMF-BO-2CoGP, is built on a multi-level CoKriging method to predict the objective function. An external binary classifier, which we approximate using a separate CoKriging model, is used to distinguish between feasible and infeasible regions. The sMF-BO-2CoGP method is demonstrated using a series of analytical examples, and a fiip-chip application for design optimization to minimize the deformation due to warping under thermal loading conditions.

Tran, Anh; Furlan, John M.; Visintainer, Robert J.; Garman, Mohamed G.; Pagalthivarthi, Krishnan V.; Cutright, Aaron C.; Wang, Yan W.

Abstract not provided.

arXiv preprint

Tran, Anh; Sun, Jing S.; Wang, Yan W.; Liu, Dehao L.; Wildey, Timothy M.

Abstract not provided.

Proceedings of the ASME Design Engineering Technical Conference

Tran, Anh; Eldred, Michael S.; McCann, Scott; Wang, Yan

Bayesian optimization (BO) is an efficient and flexible global optimization framework that is applicable to a very wide range of engineering applications. To leverage the capability of the classical BO, many extensions, including multi-objective, multi-fidelity, parallelization, and latent-variable modeling, have been proposed to address the limitations of the classical BO framework. In this work, we propose a novel multi-objective (MO) extension, called srMOBO-3GP, to solve the MO optimization problems in a sequential setting. Three different Gaussian processes (GPs) are stacked together, where each of the GP is assigned with a different task: the first GP is used to approximate a single-objective computed from the MO definition, the second GP is used to learn the unknown constraints, and the third GP is used to learn the uncertain Pareto frontier. At each iteration, a MO augmented Tchebycheff function converting MO to single-objective is adopted and extended with a regularized ridge term, where the regularization is introduced to smooth the single-objective function. Finally, we couple the third GP along with the classical BO framework to explore the richness and diversity of the Pareto frontier by the exploitation and exploration acquisition function. The proposed framework is demonstrated using several numerical benchmark functions, as well as a thermomechanical finite element model for flip-chip package design optimization.

Tran, Anh; Wildey, Timothy M.

Abstract not provided.

Wildey, Timothy M.; Bruder, Lukas B.; Bui-Thanh, Tan B.; Butler, Troy B.; Jakeman, John D.; Marvin, Brad M.; Tran, Anh; Walsh, Scott W.

Abstract not provided.

Tran, Anh; Wang, Yan W.; Furlan, John M.; Pagalthivarthi, Krishnan V.; Cutright, Aaron C.; Garman, Mohamed G.; Visintainer, Robert J.

Abstract not provided.

Acta Materialia

Tran, Anh; Tran, Hoang

Microstructure reconstruction problems are usually limited to the representation with finitely many number of phases, e.g. binary and ternary. However, images of microstructure obtained through experimental, for example, using microscope, are often represented as a RGB or grayscale image. Because the phase-based representation is discrete, more rigid, and provides less flexibility in modeling the microstructure, as compared to RGB or grayscale image, there is a loss of information in the conversion. In this paper, a microstructure reconstruction method, which produces images at the fidelity of experimental microscopy, i.e. RGB or grayscale image, is proposed without introducing any physics-based microstructure descriptor. Furthermore, the image texture is preserved and the microstructure image is represented with continuous variables (as in RGB or grayscale images), instead of binary or categorical variables, which results in a high-fidelity image of microstructure reconstruction. The advantage of the proposed method is its quality of reconstruction, which can be applied to any other binary or multiphase 2D microstructure. The proposed method can be thought of as a subsampling approach to expand the microstructure dataset, while preserving its image texture. Moreover, the size of the reconstructed image is more flexible, compared to other machine learning microstructure reconstruction method, where the size must be fixed beforehand. In addition, the proposed method is capable of joining the microstructure images taken at different locations to reconstruct a larger microstructure image. A significant advantage of the proposed method is to remedy the data scarcity problem in materials science, where experimental data is scare and hard to obtain. The proposed method can also be applied to generate statistically equivalent microstructures, which has a strong implication in microstructure-related uncertainty quantification applications. The proposed microstructure reconstruction method is demonstrated with the UltraHigh Carbon Steel micrograph DataBase (UHCSDB).

Tran, Anh; Wildey, Timothy M.; McCann, Scott M.

Abstract not provided.

Wildey, Timothy M.; Tran, Anh

Abstract not provided.

Tran, Anh; Wang, Yan W.; Wildey, Timothy M.

Abstract not provided.

Wear

Tran, Anh; Furlan, John M.; Pagalthivarthi, Krishnan V.; Visintainer, Robert J.; Wildey, Tim; Wang, Yan

Computational fluid dynamics (CFD)-based wear predictions are computationally expensive to evaluate, even with a high-performance computing infrastructure. Thus, it is difficult to provide accurate local wear predictions in a timely manner. Data-driven approaches provide a more computationally efficient way to approximate the CFD wear predictions without running the actual CFD wear models. In this paper, a machine learning (ML) approach, termed WearGP, is presented to approximate the 3D local wear predictions, using numerical wear predictions from steady-state CFD simulations as training and testing datasets. The proposed framework is built on Gaussian process (GP) and utilized to predict wear in a much shorter time. The WearGP framework can be segmented into three stages. At the first stage, the training dataset is built by using a number of CFD simulations in the order of O(102). At the second stage, the data cleansing and data mining processes are performed, where the nodal wear solutions are extracted from the solution database to build a training dataset. At the third stage, the wear predictions are made, using trained GP models. Two CFD case studies including 3D slurry pump impeller and casing are used to demonstrate the WearGP framework, in which 144 training and 40 testing data points are used to train and test the proposed method, respectively. The numerical accuracy, computational efficiency and effectiveness between the WearGP framework and CFD wear model for both slurry pump impellers and casings are compared. It is shown that the WearGP framework can achieve highly accurate results that are comparable with the CFD results, with a relatively small size training dataset, with a computational time reduction on the order of 105 to 106.

Tran, Anh

Abstract not provided.

Proceedings of the ASME Design Engineering Technical Conference

Tran, Anh; Wildey, Tim; McCann, Scott

Bayesian optimization is an effective surrogate-based optimization method that has been widely used for simulation-based applications. However, the traditional Bayesian optimization (BO) method is only applicable to single-fidelity applications, whereas multiple levels of fidelity exist in reality. In this work, we propose a bi-fidelity known/unknown constrained Bayesian optimization method for design applications. The proposed framework, called sBF-BO-2CoGP, is built on a two-level CoKriging method to predict the objective function. An external binary classifier, which is also another CoKriging model, is used to distinguish between feasible and infeasible regions. The sBF-BO-2CoGP method is demonstrated using a numerical example and a flip-chip application for design optimization to minimize the warpage deformation under thermal loading conditions.