Litcius/Paper detail

Theory-training deep neural networks for an alloy solidification benchmark problem

Mahdi Rad, Alexandre Viardin, Georg J. Schmitz, Markus Apel

2020Computational Materials Science36 citationsDOI

Topics & Concepts

Benchmark (surveying)Artificial neural networkComputer scienceField (mathematics)Deep learningArtificial intelligenceBoundary (topology)Training (meteorology)Deep neural networksBoundary value problemMachine learningMathematical optimizationAlgorithmTheoretical computer scienceMathematicsGeologyPhysicsMathematical analysisGeodesyPure mathematicsMeteorologyMachine Learning in Materials ScienceHydrogen embrittlement and corrosion behaviors in metalsNon-Destructive Testing Techniques
Theory-training deep neural networks for an alloy solidification benchmark problem | Litcius