Deep learning for reduced order modelling and efficient temporal evolution of fluid simulations
Deep learning for reduced order modelling and efficient temporal evolution of fluid simulations
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DOI：
10.1063/5.0062546
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发表时间：
2021-10-01
影响因子：
4.6
通讯作者：
Barati Farimani, Amir
中科院分区：
文献类型：
--
作者：
Pant, Pranshu;Doshi, Ruchit;Barati Farimani, Amir
降阶建模（ROM）已被广泛用于创建低阶、计算成本低廉的高阶动态系统表示。使用这些表示，rom可以有效地模拟流场，同时使用更少的参数。传统rom通过使用适当正交分解（POD）等降维技术将高阶流形线性投影到低维空间来实现这一点。在这项工作中，我们开发了一种新的深度学习框架DL-ROM（深度学习-降阶建模）来创建一个能够对降阶状态进行非线性投影的神经网络。然后，我们使用3D自动编码器和基于3D u - net的架构，使用学习到的简化状态有效地预测模拟的未来时间步长。我们的模型DL-ROM可以从学习到的ROM中创建高度精确的重建，因此能够通过在学习到的约简状态中进行时间遍历来有效地预测未来的时间步长。所有这些都是在没有基础真值监督或需要迭代求解昂贵的Navier-Stokes （NS）方程的情况下实现的，从而节省了大量的计算量。为了测试我们方法的有效性和性能，我们使用重建性能和计算运行时指标在五种不同的计算流体动力学（CFD）数据集上评估了我们的实现。DL-ROM可以将迭代求解器的计算运行时间减少近两个数量级，同时保持可接受的错误阈值。&,由AIP出版社独家授权出版。
Reduced order modeling (ROM) has been widely used to create lower order, computationally inexpensive representations of higher-order dynamical systems. Using these representations, ROMs can efficiently model flow fields while using significantly lesser parameters. Conventional ROMs accomplish this by linearly projecting higher-order manifolds to lower-dimensional space using dimensionality reduction techniques such as proper orthogonal decomposition (POD). In this work, we develop a novel deep learning framework DL-ROM (deep learning-reduced order modeling) to create a neural network capable of non-linear projections to reduced order states. We then use the learned reduced state to efficiently predict future time steps of the simulation using 3D Autoencoder and 3D U-Net-based architectures. Our model DL-ROM can create highly accurate reconstructions from the learned ROM and is thus able to efficiently predict future time steps by temporally traversing in the learned reduced state. All of this is achieved without ground truth supervision or needing to iteratively solve the expensive Navier-Stokes (NS) equations thereby resulting in massive computational savings. To test the effectiveness and performance of our approach, we evaluate our implementation on five different computational fluid dynamics (CFD) datasets using reconstruction performance and computational runtime metrics. DL-ROM can reduce the computational run times of iterative solvers by nearly two orders of magnitude while maintaining an acceptable error threshold.& nbsp;Published under an exclusive license by AIP Publishing.