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This dissertation by Tianjie Zhang focuses on the use of Physics-Informed Neural Networks (PINNs) to predict rheological properties and simulate flow behavior in 3D concrete printing (3DCP). It addresses challenges in accurately quantifying material properties and evaluating flow dynamics, proposing innovative neural network architectures such as RheologyNet and NSINN to enhance simulation efficiency and accuracy. The findings suggest that the developed PINN structures can significantly improve understandin

Author

niketzyy

Language

EN