首页|Findings from Imperial College London Provides New Data about Machine Learning ( Machine Learning and Physics-driven Modelling and Simulation of Multiphase Syste ms)
Findings from Imperial College London Provides New Data about Machine Learning ( Machine Learning and Physics-driven Modelling and Simulation of Multiphase Syste ms)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news originating from London, United Kingdom, by NewsRx correspondents, research stated, "We highlight the work of a multi-university collaborative programme, PREMIERE (PREdictive Modelling with Q uantIfication of UncERtainty for MultiphasE Systems), which is at the intersecti on of multi-physics and machine learning, aiming to enhance predictive capabilit ies in complex multiphase flow systems across diverse length and time scales. Ou r contributions encompass a variety of approaches, including the Design of Exper iments for nanoparticle synthesis optimisation, Generalised Latent Assimilation models for drop coalescence prediction, Bayesian regularised artificial neural n etworks, eXtreme Gradient Boosting for microdroplet formation prediction, and a sub-sampling based adversarial neural network for predicting slug flow behaviour in twophase pipe flows."
LondonUnited KingdomEuropeCyborgsEmerging TechnologiesMachine LearningImperial College London
NETL
NSTL
