化工釜残非牛顿流体流变特性的输入凸神经网络建模Input-convex neural network modeling of rheological properties of non-Newtonian fluids in chemical distillation residues

发表于《西安交通大学学报》2025 年第 59 卷第 12 期。针对化工釜残等非牛顿流体，建立输入凸神经网络（ICNN）流变本构模型，实现流变特性的快速准确预测。DOI: 10.7652/xjtuxb202512007。

Published in the Journal of Xi'an Jiaotong University (Vol. 59, No. 12, 2025). An input-convex neural network (ICNN) rheological constitutive model is established for non-Newtonian fluids such as chemical distillation residues, enabling fast and accurate prediction of rheological properties. DOI: 10.7652/xjtuxb202512007.