Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

• Published in: Computer methods in applied mechanics and engineering Vol. 361; p. 112732
• Main Authors: Sun, Luning, Gao, Han, Pan, Shaowu, Wang, Jian-Xun
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.04.2020; Elsevier BV
• Subjects: Artificial neural networks; Boundary conditions; Cardiovascular flows; Computational fluid dynamics; Computer simulation; Data points; Deep learning; Design optimization; First principles; Fluid dynamics; Fluid flow; Hemodynamics; Internal flow; Label-free; Machine learning; Mathematical models; Meshing; Navier-Stokes; Navier-Stokes equations; Neural networks; Partial differential equations; Physics; Physics-informed machine learning; Polynomials; Simulation; Training; Uncertainty; Uncertainty quantification; Physics-informed machine learning; Uncertainty quantification; Navier-Stokes; Cardiovascular flows; Label-free; Neural networks
• ISSN: 0045-7825; 1879-2138
• DOI: 10.1016/j.cma.2019.112732

Numerical simulations on fluid dynamics problems primarily rely on spatially or/and temporally discretization of the governing equation using polynomials into a finite-dimensional algebraic system. Due to the multi-scale nature of the physics and sensitivity from meshing a complicated geometry, such process can be computational prohibitive for most real-time applications (e.g., clinical diagnosis and surgery planning) and many-query analyses (e.g., optimization design and uncertainty quantification). Therefore, developing a cost-effective surrogate model is of great practical significance. Deep learning (DL) has shown new promises for surrogate modeling due to its capability of handling strong nonlinearity and high dimensionality. However, the off-the-shelf DL architectures, success of which heavily relies on the large amount of training data and interpolatory nature of the problem, fail to operate when the data becomes sparse. Unfortunately, data is often insufficient in most parametric fluid dynamics problems since each data point in the parameter space requires an expensive numerical simulation based on the first principle, e.g., Navier–Stokes equations. In this paper, we provide a physics-constrained DL approach for surrogate modeling of fluid flows without relying on any simulation data. Specifically, a structured deep neural network (DNN) architecture is devised to enforce the initial and boundary conditions, and the governing partial differential equations (i.e., Navier–Stokes equations) are incorporated into the loss of the DNN to drive the training. Numerical experiments are conducted on a number of internal flows relevant to hemodynamics applications, and the forward propagation of uncertainties in fluid properties and domain geometry is studied as well. The results show excellent agreement on the flow field and forward-propagated uncertainties between the DL surrogate approximations and the first-principle numerical simulations. •Proposed a simulation-free, physics-constrained deep learning for surrogate CFD model.•Boundary-encoded neural network outperforms the one with soft boundary constraints.•Demonstrated effectiveness of the label-free learning on a few vascular flows.