首页|Findings from School of Automation Has Provided New Data on Machine Learning (Ti me/space Separation-based Physics-informed Machine Learning for Spatiotemporal M odeling of Distributed Parameter Systems)
Findings from School of Automation Has Provided New Data on Machine Learning (Ti me/space Separation-based Physics-informed Machine Learning for Spatiotemporal M odeling of Distributed Parameter Systems)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Researchers detail new data in Machine Learning. According to news reporting originatingin Changsha, People’s Republi c of China, by NewsRx journalists, research stated, “This articleintroduces a n ovel time/space separation-based physics-informed machine learning (T/S-PIML) mo delingmethod by making full use of the complementary strengths of the physics-i nformed neural network (PINN)and the time/space separation methodology. T/S-PIM L is the first attempt to seamlessly integrate structural(including spatial and temporal) physical information with data for effective spatiotemporal modelingof distributed parameter systems (DPSs).”
ChangshaPeople’s Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningSchool of Automation
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