45 citations · 184 across the 21 of their papers we have counts for
21 papers
A Continual Validation, Updating, and Decision-Making Framework for Self-Adaptive Digital Twins via Robust Model Predictive Control: A Case Study in Additive Manufacturing
Yi-Ping Chen, Ying-Kuan Tsai, Vispi Karkaria +3
Digital Twins rely on surrogate models to mirror physical systems in real time, yet these models can degrade as operating conditions evolve, a phenomenon known as concept drift. Ma…
Bootstrapped Control Limits for Score-Based Concept Drift Control Charts
Jiezhong Wu, Daniel W. Apley
Monitoring for changes in a predictive relationship represented by a fitted supervised learning model (i.e., concept drift detection) is a widespread problem in modern data-driven…
One-at-a-time knockoffs: controlled false discovery rate with higher power
Charlie K. Guan, Zhimei Ren, Daniel W. Apley
We propose one-at-a-time knockoffs (OATK), a new methodology for detecting important explanatory variables in linear regression models while controlling the false discovery rate (F…
A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images
Kungang Zhang, Wei Chen, Wing K. Liu +2
Microstructure of materials is often characterized through image analysis to understand processing-structure-properties linkages. We propose a largely automated framework that inte…
Emerging Microelectronic Materials by Design: Navigating Combinatorial Design Space with Scarce and Dispersed Data
Hengrui Zhang, Alexandru B. Georgescu, Suraj Yerramilli +5
The increasing demands of sustainable energy, electronics, and biomedical applications call for next-generation functional materials with unprecedented properties. Of particular in…
Simulation-Free Determination of Microstructure Representative Volume Element Size via Fisher Scores
Wei Liu, Satyajit Mojumder, Wing Kam Liu +2
A representative volume element (RVE) is a reasonably small unit of microstructure that can be simulated to obtain the same effective properties as the entire microstructure sample…
Interpretable Architecture Neural Networks for Function Visualization
Shengtong Zhang, Daniel W. Apley
In many scientific research fields, understanding and visualizing a black-box function in terms of the effects of all the input variables is of great importance. Existing visualiza…
Fully Bayesian inference for latent variable Gaussian process models
Suraj Yerramilli, Akshay Iyer, Wei Chen +1
Real engineering and scientific applications often involve one or more qualitative inputs. Standard Gaussian processes (GPs), however, cannot directly accommodate qualitative input…
Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design
Hengrui Zhang, Wei Wayne Chen, Akshay Iyer +2
Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure,…
Diversity Subsampling: Custom Subsamples from Large Data Sets
Boyang Shang, Daniel W. Apley, Sanjay Mehrotra
Subsampling from a large data set is useful in many supervised learning contexts to provide a global view of the data based on only a fraction of the observations. Diverse (or spac…
Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors
Liwei Wang, Suraj Yerramilli, Akshay Iyer +3
Scientific and engineering problems often require the use of artificial intelligence to aid understanding and the search for promising designs. While Gaussian processes (GP) stand…
Nonstationarity Analysis of Materials Microstructures via Fisher Score Vectors
Kungang Zhang, Daniel W. Apley, Wei Chen
Microstructures are critical to the physical properties of materials. Stochastic microstructures are commonly observed in many kinds of materials and traditional descriptor-based i…
Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors
Kungang Zhang, Anh T. Bui, Daniel W. Apley
Supervised learning models are one of the most fundamental classes of models. Viewing supervised learning from a probabilistic perspective, the set of training data to which the mo…
Database, Features, and Machine Learning Model to Identify Thermally Driven Metal-Insulator Transition Compounds
Alexandru B. Georgescu, Peiwen Ren, Aubrey R. Toland +6
Metal-insulator transition (MIT) compounds are materials that may exhibit insulating or metallic behavior, depending on the physical conditions, and are of immense fundamental inte…
Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables
Yichi Zhang, Daniel Apley, Wei Chen
Although Bayesian Optimization (BO) has been employed for accelerating materials design in computational materials engineering, existing works are restricted to problems with quant…
Density Deconvolution with Additive Measurement Errors using Quadratic Programming
Ran Yang, Daniel Apley, Jeremy Staum +1
Distribution estimation for noisy data via density deconvolution is a notoriously difficult problem for typical noise distributions like Gaussian. We develop a density deconvolutio…
A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors
Yichi Zhang, Siyu Tao, Wei Chen +1
Computer simulations often involve both qualitative and numerical inputs. Existing Gaussian process (GP) methods for handling this mainly assume a different response surface for ea…
A monitoring and diagnostic approach for stochastic textured surfaces
Anh Tuan Bui, Daniel W. Apley
We develop a supervised-learning-based approach for monitoring and diagnosing texture-related defects in manufactured products characterized by stochastic textured surfaces that sa…
Patchwork Kriging for Large-scale Gaussian Process Regression
Chiwoo Park, Daniel Apley
This paper presents a new approach for Gaussian process (GP) regression for large datasets. The approach involves partitioning the regression input domain into multiple local regio…
Designed Sampling from Large Databases for Controlled Trials
Liwen Ouyang, Daniel W. Apley, Sanjay Mehrotra
The increasing prevalence of rich sources of data and the availability of electronic medical record databases and electronic registries opens tremendous opportunities for enhancing…
Local Gaussian process approximation for large computer experiments
Robert B. Gramacy, Daniel W. Apley
We provide a new approach to approximate emulation of large computer experiments. By focusing expressly on desirable properties of the predictive equations, we derive a family of l…