Abstract
Computational methods and tools for scientific and engineering applications must be able to ingest and interpret domain knowledge, conceptual constructs, and theoretical frameworks. The artificial intelligence tool built for materials science applications (pyroMind.2020) exploits a variety of novel computational methods to learn latent patterns and empirical relationships to complement scarce experimental data. Learning to predict the latent variables (latent learning) helps to pinpoint the anticipated range-edge anomalies and improves the confidence in data interpretation, interpolation, and extrapolation.
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