首页|Recent Findings from University of California Berkeley Provides New Insights into Machine Learning (A Voxel-based Machine-learning Framework for Thermo-fluidic Identification of Unknown Objects)
Recent Findings from University of California Berkeley Provides New Insights into Machine Learning (A Voxel-based Machine-learning Framework for Thermo-fluidic Identification of Unknown Objects)
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Elsevier
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Berkeley, California, by NewsRx editors, the research stated, “The rapid iden- tification of unknown objects by their thermo-fluid flow field signature is becoming increasingly more important. In this work, a machine-learning framework is developed that efficiently simulates and adapts object geometries in order to match the thermo-flow field signature generated by an unknown object, across a time series of voxel-frames.” Funders for this research include UC Berkeley College of Engineering, USA, Sandia National Labs, USA. Our news editors obtained a quote from the research from the University of California Berkeley, “In order to achieve this, a thermo-fluid model is developed, based on the Navier-Stokes equations and the first law of thermodynamics, using a voxel rendering of the system, which is rapidly solved with a voxel-tailored, temporally-adaptive, iterative solution scheme. This voxel-framework is then combined with a genomic- based machine-learning algorithm to develop a digital-twin (digital-replica) of the system that can run in real-time or faster than the actual physical system.”
BerkeleyCaliforniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of California Berkeley
NETL
NSTL
Elsevier
