使用孪生神经网络的复杂边界受限流体多尺度建模框架
Multiscale modeling framework of a constrained fluid with complex boundaries using twin neural networks
AI总结：
本文提出孪生神经网络增强的多尺度框架，通过神经网络表示粗粒化势并代理预测复杂边界下受限流体的速度剖面，实现原子尺度与介观尺度模拟的准确连接。
摘要：
受限流体的性质在从材料到生物学的各类应用中日益受到关注。在本工作中，我们提出一种使用孪生神经网络的多尺度模型，用于研究被限制在具有复杂形状的固体表面之间的流体的性质。原子尺度模型与介观尺度模型通过粗粒化势相连接，该势由第一个神经网络表示。随后，我们训练第二个神经网络模型作为代理模型，以在介观尺度上预测具有复杂边界条件的受限流体的速度剖面。我们系统研究了复杂边界条件对流体动力学性质的影响以及神经网络模型预测的准确性。结果表明，神经网络增强的多尺度框架能够连接原子尺度和介观尺度的模拟，并在介观尺度上再现受限流体的性质。本工作为借助机器学习技术开发多尺度模型提供了见解，所开发的模型可用于提高石油采收率和多孔材料设计等现代纳米技术应用。
英文摘要：
The properties of constrained fluids have increasingly gained relevance for applications ranging from materials to biology. In this work, we propose a multiscale model using twin neural networks to investigate the properties of a fluid constrained between solid surfaces with complex shapes. The atomic scale model and the mesoscale model are connected by the coarse-grained potential which is represented by the first neural network. Then we train the second neural network model as a surrogate to predict the velocity profile of the constrained fluid with complex boundary conditions at the mesoscale. The effect of complex boundary conditions on the fluid dynamics properties and the accuracy of the neural network model prediction are systematically investigated. We demonstrate that the neural network-enhanced multiscale framework can connect simulations at atomic scale and mesoscale and reproduce the properties of a constrained fluid at mesoscale. This work provides insight into multiscale model development with the aid of machine learning techniques and the developed model can be used for modern nanotechnology applications such as enhanced oil recovery and porous materials design.
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