A new file format, BP5, and accompanying serialization class has been developed in the ADaptable I/O System (ADIOS) framework.
A graph convolutional neural network (GCNN) was trained with millions of molecules to accurately predict molecular photo-optical properties by scaling data loading and training to over 1,500 GPUs on the Summit and Perlmutter supercomputers at the OLCF a
In June 2022, Chemical Security Assessment Tool (CSAT) Primary Systems Team members implemented the new STIG Compliance Tool (SCT) the team designed to automate—by documenting and continuously monitoring—Oracle database compliance with the Security Tech
Analyzing the logs of even the smallest Information Technology (IT) system can be a challenge, considering that they can generate millions of lines of log data in a very short time.
91°µÍø researchers developed an invertible neural network (INN) to effectively and efficiently solve earth-system model calibration and simulation problems.
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.
