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)
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).”
Key words
Changsha/People’s Republic of China/As ia/Cyborgs/Emerging Technologies/Machine Learning/School of Automation
NETL
NSTL