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Predicting Metallic Defects with Machine Learning

Atlantis On Shuttle Carrier Craft Aerospace Aircraft 577851

For the first time, Berkeley Lab researchers have built and trained machine learning algorithms to predict defect behavior in certain intermetallic compounds with high accuracy. This method will accelerate research of new advanced alloys and lightweight new materials for applications spanning automotive to aerospace and much more. » Read More

Predicting Defects with Machine Learning

Atlantis On Shuttle Carrier Craft Aerospace Aircraft 577851

For the first time, Berkeley Lab researchers have built and trained machine learning algorithms to predict defect behavior in certain intermetallic compounds with high accuracy. This method will accelerate research of new advanced alloys and lightweight new materials for applications spanning automotive to aerospace and much more. » Read More

Invisible Chaos of Superluminous Supernovae

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To better understand the physical conditions that create superluminious supernova, astrophysicists are running 2D simulations of these events using supercomputers at NERSC and CRD developed CASTRO code. » Read More

Berkeley Lab to Lead AMR Co-Design Center

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Berkeley Lab will lead one of four co-design centers under the Department of Energy’s Exascale Computing Project (ECP). The Block-Structured Adaptive Mesh Refinement Co-Design Center will be led by John Bell of the lab’s Computational Research Division, with support from Argonne National Laboratory and the National Renewable Energy Laboratory. » Read More

CCSI Toolset Wins 2016 R&D100 Award

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The CCSI Toolset—a suite of computational tools and models designed to accelerate the development of cost-effective carbon capture technology—has been awarded a 2016 R&D100 Award. The Toolset was developed by a collaboration led by the National Energy Technology Laboratory (NETL), and included multiple Department of Energy National Laboratories, including Berkeley Lab. » Read More

News

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Machine Learning Method Accurately Predicts Metallic Defects

February 3, 2017

For the first time, researchers at Berkeley Lab have built and trained machine learning algorithms to predict defect behavior in certain intermetallic compounds with high accuracy. This method will accelerate research of new advanced alloys and lightweight new materials for applications spanning automotive to aerospace and much more.

mag breakv2

Simulations Reveal the Invisible Chaos of Superluminous Supernovae

February 1, 2017

To better understand the physical conditions that create superluminious supernova, astrophysicists are running 2D simulations of these events using supercomputers at NERSC and CRD developed CASTRO code.


India heat wave map fixed

Simulations Confirm Observations of 2015 India/Pakistan Heat Waves

December 15, 2016

A paper published December 15 during the American Geophysical Union (AGU) fall meeting in San Francisco points to new evidence of human influence on extreme weather events.

Graph500

CRD’s Ibrahim Cracks Graph500 with Precisely Tuned Data-Intensive Algorithms

December 8, 2016

When the latest version of the Graph 500 list was released Nov. 16 at the SC16 conference, there were two new entries in the top 10, both contributed by Khaled Ibrahim of Berkeley Lab’s Computational Research Division.