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Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations

Harsh Bhatia, Timothy S. Carpenter, Helgi I. Ingólfsson, Gautham Dharuman, Piyush Karande, Shusen Liu, Tomas Oppelstrup, Chris Neale, Felice C. Lightstone, Brian Van Essen, James N. Glosli, Peer‐Timo Bremer

2021Nature Machine Intelligence38 citationsDOIOpen Access PDF

Topics & Concepts

Computer scienceMicroscale chemistryScalabilityContext (archaeology)MacroAdaptive samplingMultiscale modelingBridge (graph theory)Sampling (signal processing)Scale (ratio)SupercomputerComputational scienceArtificial intelligenceMachine learningDistributed computingParallel computingMonte Carlo methodProgramming languageFilter (signal processing)PhysicsComputer visionBiologyStatisticsMathematics educationChemistryPaleontologyMathematicsInternal medicineQuantum mechanicsMedicineDatabaseComputational chemistryProtein Structure and DynamicsTheoretical and Computational PhysicsAdvanced Mathematical Modeling in Engineering
Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations | Litcius