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LLNL-Developed Software Leverages Exascale Power to Optimize National Grid's Emergency Response

LLNL-Developed Software Leverages Exascale Power to Optimize National Grid's Emergency Response
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Cosmin Petra , Jingyi Frank Wang , Ignacio Aravena Solis , Pacific Northwest National Laboratory , Optimization Group , Oak Ridge National Laboratory , Lawrence Livermore National Laboratory , National Renewable Energy Laboratory , Laboratory Directed Research , Argonne National Laboratory , Exascale Computing Project , Oak Ridge Leadership Computing , Argonne National , Uncertainty Quantification , Applied Scientific Computing ,

"Evidence-based uncertainty quantification for bending properties of bi" by Zhou Li, Lixiong Cao et al.

Bimetal or laminated metal composites consisting of several metal or alloy layers can efficiently absorb the advantageous properties of each metal when utilised as raw materials for future remanufacturing. Their mechanical response characteristics and failure modes face uncertain challenges during the processing owing to the existence of heterogenous interfacial transition zones between layers. This work aims to study the uncertainty quantification in bending processing of a novel bimetal composite as a result of uncertain interfacial zones using an efficient evidence-based reliability analysis method. An analytical model for bending characteristics of bimetal composite was first established based on the deterministic material properties of each component layer, and it was employed to quantitatively analyse the bending uncertainty properties of 2205 duplex stainless steel/AH36 carbon steel bimetal composite (2205/AH36 BC) considering the epistemic uncertainty in geometric dimensions an ....

Bending Properties , Bimetal Composite , Vidence Theory , Nterfacial Zone , Uncertainty Quantification ,

Overcoming the Challenges of Ocean Data Uncertainty

In oceanography, as in any scientific field, the goal is not to eliminate uncertainty in data, but instead to better quantify and clearly communicate its size and nature. ....

United States , United Kingdom , University Of Washington , Shane Elipot , Mike Patterson , Aneesh Subramanian , Kyla Drushka , World Climate Research Program , Global Synthesis Observation Panel Ocean Reanalysis Intercomparison , University Of Miami , Department Of Atmospheric , A National Strategy For Advancing Climate Modeling , Intergovernmental Panel On Climate Change , Committee For Guides , Predictability Program , National Research Council , Intergovernmental Panel On Climate , Us Global Change Research Program , Cultural Organization Global Ocean Observing System , Oceanic Sciences , Contribution Of Working Group , Physics Laboratory , Solutions Are , Rosenstiel School Of Marine , University Of Colorado Boulder , International Quality ,

Efficient technique improves machine-learning models' reliability | MIT News | Massachusetts Institute of Technology

A new technique can enable a machine-learning model to quantify how confident it is in its predictions, but does not require vast troves of new data and is much less computationally intensive than other techniques. The work was led by researchers from MIT and the MIT-IBM Watson AI Lab. ....

Soumya Ghosh , Gregory Wornell , Maohao Shen , Prasanna Sattigeri , Subhro Das , Watson Ai Lab , Research Laboratory Of Electronics , University Of Florida , Algorithms Laboratory , Us National Science Foundation , Research Laboratory , Sumitomo Professor , Machine Learning , Explainable Ai , Uncertainty Quantification , Peta Model ,

Nobakht, B.

Nobakht, B.
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Glasgow City , United Kingdom , Behzad Nobakht , Sharif University Of Technology , Polytechnic University Of Turin , Watt University , D National Engineering Laboratory , Data Scientist , Sharif University , Polytechnic University , Uncertainty Quantification , Professor Mike Christie ,