学习非牛顿流体的未知物理
Learning Unknown Physics of non-Newtonian Fluids
摘要：
我们扩展了物理信息神经网络（PINN）方法，仅使用速度测量来学习两个非牛顿系统（聚合物熔体和颗粒悬浮液）的粘度模型。PINN推断的粘度模型同意与经验模型的剪切速率与大的绝对值，但偏离剪切速率接近零的分析模型具有非物理奇异性。一旦学习了粘度模型，我们使用PINN方法仅使用边界条件来求解非牛顿流体流动的动量守恒方程。
英文摘要：
We extend the physics-informed neural network (PINN) method to learn viscosity models of two non-Newtonian systems (polymer melts and suspensions of particles) using only velocity measurements. The PINN-inferred viscosity models agree with the empirical models for shear rates with large absolute values but deviate for shear rates near zero where the analytical models have an unphysical singularity. Once a viscosity model is learned, we use the PINN method to solve the momentum conservation equation for non-Newtonian fluid flow using only the boundary conditions.
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