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Found 9 papers, 2 papers with code
Data-Centric Mixed-Variable Bayesian Optimization For Materials Design
To this end, we present a data-centric, mixed-variable Bayesian Optimization framework that integrates data from literature, experiments, and simulations for knowledge discovery and computational materials design.
A Conditional Generative Model for Predicting Material Microstructures from Processing Methods
Microstructures of a material form the bridge linking processing conditions - which can be controlled, to the material property - which is the primary interest in engineering applications.
Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors
In addition, an interpretable latent space is obtained to draw insights into the effect of categorical factors, such as those associated with building blocks of architectures and element choices in metamaterial and materials design.
An Automated System for Detecting Visual Damages of Wind Turbine Blades
We discuss the performance of our damage's suggestion model in production and how this system works in coordination with humans as part of a commercialized product and how it can contribute towards lowering wind energy's operational costs.
Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design
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, and synthesis methods.
Learning to estimate a surrogate respiratory signal from cardiac motion by signal-to-signal translation
Our goal is to lay a foundation to guide the optimization of neural networks for respiratory motion correction from SPECT without the need for an EMT.
Learning to identify cracks on wind turbine blade surfaces using drone-based inspection images
Hence in this work, we bring more objectivity, scalability, and repeatability in our damage inspection process, using deep learning, to miss fewer cracks.
Fully Bayesian inference for latent variable Gaussian process models
However, this plug-in approach will not account for uncertainty in estimation of the LVs, which can be significant especially with limited training data.
Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothing
The main challenge in this estimation task is the potential confounding of treatment assignment with an individual's covariates in the training data, whereas during inference ICTE requires prediction on independently sampled treatments.
