首页|New Findings from Technical University Munich (TU Munich)Describe Advances in M achine Learning (A Parameterized Physicsinformed Machine Learning Approach for Solving Heat and Mass Transfer Equations In the Drying Process)
New Findings from Technical University Munich (TU Munich)Describe Advances in M achine Learning (A Parameterized Physicsinformed Machine Learning Approach for Solving Heat and Mass Transfer Equations In the Drying Process)
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 – Investigators publish new report on Ma chine Learning.According to news reporting originating from Munich, Germany, by NewsRx correspondents, research stated, “This paper introduces a novel applicat ion for surrogate modeling in the context of coupled heat and mass transfer duri ng drying using physics-informed neural networks (PINNs).The Luikov model is im plemented within the PINNs model to simulate heat and mass transfer dynamics.”
MunichGermanyEuropeCyborgsEmergi ng TechnologiesMachine LearningTechnical University Munich (TU Munich)
NETL
NSTL
