首页|Data from Eastern Institute of Technology Advance Knowledge in Machine Learning (Forward Prediction and Surrogate Modeling for Subsurface Hydrology: a Review of Theory-guided Machine-learning Approaches)
Data from Eastern Institute of Technology Advance Knowledge in Machine Learning (Forward Prediction and Surrogate Modeling for Subsurface Hydrology: a Review of Theory-guided Machine-learning Approaches)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news reporting originating from Zhejiang, People’s Repu blic of China, by NewsRx correspondents, research stated, “Forward prediction of subsurface hydrological processes, as well as tasks that require repetitive eva luation of forward models (e.g., uncertainty quantification, inverse modeling, o ptimization problems, etc.) can be computationally-intensive. Deep learning-base d approaches are increasingly applied to facilitate the forward modeling process (e.g., learning partial differential equations) and to build surrogate models a s efficient replacements of physics-based forward models.” Our news editors obtained a quote from the research from the Eastern Institute o f Technology, “However, for practical problems, the available training data are usually of limited quantity and quality, which significantly affects the models’ learning capability. Theory-guided machine-learning (TGML) emerged in the past decades to cope with such conditions. Through introducing theoretical guidance t o data preprocessing, neural network design, or model-training processes, the ac curacy, robustness, and generalizability of the trained model can be dramaticall y improved. Herein, a review is provided on the latest advances of TGML applied to subsurface hydrology problems. In particular, three ways of incorporating the oretical guidance into the model-training process are summarized, all of which a re based on the principle of adding additional physical regularizations to the l oss function. TGML-based surrogate models for forward prediction, uncertainty qu antification, inverse modeling, and optimization problems in the areas of single -/two-phase flow and contaminant transport are reviewed in detail.”
ZhejiangPeople’s Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningEastern Institute of Techn ology
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