![Understanding uncertainty in microstructure evolution and constitutive properties in additive process modeling Computational Sciences and Engineering Division CSED ORNL](/sites/default/files/styles/list_page_thumbnail/public/2022-08/understanding_uncertainty_in_microstructure_evolution_and_constitutive_properties_in_additive_process_modeling.png?h=3d1a8d7c&itok=yn-39vaH)
Simulations of Inconel 625 microstructure development and constitutive properties during Selective Laser Melting processing were performed utilizing two exascale-capable codes on the pre-exascale Summit supercomputer.
Simulations of Inconel 625 microstructure development and constitutive properties during Selective Laser Melting processing were performed utilizing two exascale-capable codes on the pre-exascale Summit supercomputer.
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.
ORNL researchers developed a novel nonlinear level set learning method to reduce dimensionality in high-dimensional function approximation.
Researchers at ORNL have created a unique simulation technology that allows software systems to participate in slower than real time simulation exercises, and to accomplish this without requiring recompilation of source code, relinking of object files,