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physics.comp-ph2018
A graph theoretic framework for representation, exploration and analysis on computed states of physical systems
R. Banerjee, K. Sagiyama, G. H. Teichert +1
A graph theoretic perspective is taken for a range of phenomena in continuum physics in order to develop representations for analysis of large scale, high-fidelity solutions to the…
physics.comp-ph2018
Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics
Gregory Teichert, Krishna Garikipati
Machine learning has been effective at detecting patterns and predicting the response of systems that behave free of natural laws. Examples include learning crowd dynamics, recomme…