A graph convolutional neural network (GCNN) was trained to accurately predict formation energy and mechanical properties of solid solution alloys crystallized in different lattice structures, thereby advancing the design of alloys for improving mechanic
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In this work we focus on dynamics problems described by waves, i.e. by hyperbolic partial differential equations.
A research team from ORNL, Pacific Northwest National Laboratory, and Arizona State University has developed a novel method to detect out-of-distribution (OOD) samples in continual learning without forgetting the learned knowledge of preceding tasks.
Efforts to bring ORNL’s wireless sensor platform to market are on target and proceeding as planned.