Hariprasad Kodamana

TL;DR: In this paper, Gaussian Process Regression (GPR) was used to predict Young's modulus for silicate glasses having a sparse dataset and showed that GPR significantly outperforms NN for the sparse dataset while ensuring no overfitting.

TL;DR: The paper presents insightful equivalence between the classical multivariate techniques for process monitoring and their probabilistic counterparts, which is obtained by restricting the generalized model.

TL;DR: In this article, a large dataset of glasses comprising of up to 37 oxide components and more than 100,000 glass compositions was used to develop a series of new design charts, termed as glass selection charts, which enable the rational design of functional glasses for targeted applications by identifying unique compositions that satisfy two or more constraints, on both compositions and properties.

TL;DR: A general introduction to the probabilistic methods for robust identification is provided, the main steps involved in the development of models are illustrated, and the related literature is reviewed.

TL;DR: This work proposes to employ a mixture of two Gaussian distributions as the noise model to capture both regular noise and irregular noise, thereby enhancing the robustness of the regression model.