Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties
George Mason University（乔治梅森大学）；Trinity College Dublin（都柏林圣三一学院）；Dublin Institute for Advanced Studies（都柏林高等研究院）；University of Oxford（牛津大学）；Center for Mathematics and Computer Science (CWI)（数学与计算机科学中心（CWI））；Stanford University（斯坦福大学）；Element AI；Southwest Research Institute（西南研究院）
英文摘要：
Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient. However in many scientific domains this is not adequate and estimations of errors and uncertainties are crucial. To address this issue we propose a Bayesian framework that decomposes uncertainties into epistemic and aleatoric uncertainties. We test the validity of our approach by super-resolving images of the Sun's magnetic field and by generating maps measuring the range of possible high resolution explanations compatible with a given low resolution magnetogram.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
