Introduction to Optimizing Polymer Tg Machine Learning With Uncertainty Quantification
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Optimizing Polymer Tg Machine Learning With Uncertainty Quantification Comprehensive Overview
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In this lecture, we will motivate why the successful application of
Summary & Highlights for Optimizing Polymer Tg Machine Learning With Uncertainty Quantification
- Gaussian process regression (GPR) is a probabilistic approach to making predictions. GPRs are easy to implement, flexible, and ...
- Machine/
- Neural networks are infamous for making wrong predictions with high confidence. Ideally, when a model encounters difficult ...
- Presented by Lalitha Venkataramanan, Scientific Advisor at Schlumberger. Abstract:
- Contributed presentation at 2021 IAP conference "Debating the potential of
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