首页|Data on Machine Learning Discussed by a Researcher at Idaho State University (As sessment of Machine Learning Techniques for Simulating Reacting Flow: From Plasm a-Assisted Ignition to Turbulent Flame Propagation)
Data on Machine Learning Discussed by a Researcher at Idaho State University (As sessment of Machine Learning Techniques for Simulating Reacting Flow: From Plasm a-Assisted Ignition to Turbulent Flame Propagation)
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 – Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from Poca tello, Idaho, by NewsRx correspondents, research stated, “Combustion involves th e study of multiphysics phenomena that includes fluid and chemical kinetics, che mical reactions and complex nonlinear processes across various time and space sc ales.” Funders for this research include Internal Grant of Idaho State University. Our news correspondents obtained a quote from the research from Idaho State Univ ersity: “Accurate simulation of combustion is essential for designing energy con version systems. Nonetheless, due to its multiscale, multiphysics nature, simula ting these systems at full resolution is typically difficult. The massive and co mplex data generated from experiments and simulations, particularly in turbulent combustion, presents both a challenge and a research opportunity for advancing combustion studies. Machine learning facilitates data-driven techniques to manag e the substantial amount of combustion data that is either obtained through expe riments or simulations, and thereby can find the hidden patterns underlying thes e data. Alternatively, machine learning models can be useful to make predictions with comparable accuracy to existing models, while reducing computational costs significantly. In this era of big data, machine learning is rapidly evolving, o ffering promising opportunities to explore its integration with combustion resea rch.”
Idaho State UniversityPocatelloIdahoUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMach ine Learning
NETL
NSTL
