首页|Study Findings on Machine Learning Are Outlined in Reports from University of Texas Austin (Bayesian Model Calibration for Diblock Copolymer Thin Film Self-assembly Using Power Spectrum of Microscopy Data and Machine Learning Surrogate)
Study Findings on Machine Learning Are Outlined in Reports from University of Texas Austin (Bayesian Model Calibration for Diblock Copolymer Thin Film Self-assembly Using Power Spectrum of Microscopy Data and Machine Learning Surrogate)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Fresh data on Machine Learning are presented in a new report. According to newsreporting out of Austin, Texas, by NewsRx editors, research stated, “Identifying parameters of computationalmodels from experimental data, or model calibration, is fundamental for assessing and improvingthe predictability and reliability of computer simulations. In this work, we propose a method for Bayesiancalibration of models that predict morphological patterns of diblock copolymer (Di-BCP) thin film selfassemblywhile accounting for various sources of uncertainties in pattern formation and data acquisition.”
AustinTexasUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Texas Austin
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