Search Results for author: Satish Bukkapatnam

Found 5 papers, 0 papers with code

Machine Learning-Enhanced Prediction of Surface Smoothness for Inertial Confinement Fusion Target Polishing Using Limited Data

no code implementations16 Dec 2023 Antonios Alexos, Junze Liu, Akash Tiwari, Kshitij Bhardwaj, Sean Hayes, Pierre Baldi, Satish Bukkapatnam, Suhas Bhandarkar

In Inertial Confinement Fusion (ICF) process, roughly a 2mm spherical shell made of high density carbon is used as target for laser beams, which compress and heat it to energy levels needed for high fusion yield.

EBLIME: Enhanced Bayesian Local Interpretable Model-agnostic Explanations

no code implementations29 Apr 2023 Yuhao Zhong, Anirban Bhattacharya, Satish Bukkapatnam

We propose EBLIME to explain black-box machine learning models and obtain the distribution of feature importance using Bayesian ridge regression models.

Feature Importance regression +1

Unsupervised spectral-band feature identification for optimal process discrimination

no code implementations7 Dec 2022 Akash Tiwari, Satish Bukkapatnam

Given continuous spectra of two classes $A$ and $B$, or in general, two stochastic processes $S^{(A)}(f)$ and $S^{(B)}(f)$, $f \in \mathbb{R}^+$, we address the ubiquitous problem of identifying a subset of intervals of $f$ called spectral-bands $\underline{\alpha} \subset \mathbb{R}^+$ such that the energies $\textbf{E}(\underline{\alpha})$ of these bands can optimally discriminate between the two classes.

Towards Futuristic Autonomous Experimentation--A Surprise-Reacting Sequential Experiment Policy

no code implementations1 Dec 2021 Imtiaz Ahmed, Satish Bukkapatnam, Bhaskar Botcha, Yu Ding

An autonomous experimentation platform in manufacturing is supposedly capable of conducting a sequential search for finding suitable manufacturing conditions for advanced materials by itself or even for discovering new materials with minimal human intervention.

Bayesian Optimization

Consistent estimation of the max-flow problem: Towards unsupervised image segmentation

no code implementations1 Nov 2018 Ashif Sikandar Iquebal, Satish Bukkapatnam

In this work, we present a fully unsupervised segmentation approach using a continuous max-flow formulation over the image domain while optimally estimating the flow parameters from the image characteristics.

Brain Tumor Segmentation Decision Making +4

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