Explicit physics-informed neural networks for non-linear upscaling closure: the case of transport in tissues

Abstract

In this work, we use a combination of formal upscaling and data-driven machine learning for explicitly closing a nonlinear transport and reaction process in a multiscale tissue. The classical effectiveness factor model is used to formulate the macroscale reaction kinetics. We train a multilayer perceptron network using training data generated by direct numerical simulations over microscale examples. Once trained, the network is used for numerically solving the upscaled (coarse-grained) differential equation describing mass transport and reaction in two example tissues. The network is described as being explicit in the sense that the network is trained using macroscale concentrations and gradients of concentration as components of the feature space.Network training and solutions to the macroscale transport equations were computed for two different tissues. The two tissue types (brain and liver) exhibit markedly different geometrical complexity and spatial scale (cell size and sample size). The upscaled solutions for the average concentration are compared with numerical solutions derived from the microscale concentration fields by a posteriori averaging. There are two outcomes of this work of particular note: 1) we find that the trained network exhibits good generalizability, and it is able to predict the effectiveness factor with high fidelity for realistically-structured tissues despite the significantly different scale and geometry of the two example tissue types; and 2) the approach results in an upscaled PDE with an effectiveness factor that is predicted (implicitly) via the trained neural network. This latter result emphasizes our purposeful connection between conventional averaging methods with the use of machine learning for closure; this contrasts with some machine learning methods for upscaling

A composable machine-learning approach for steady-state simulations on high-resolution grids

“…These method use Automatic Differentiation (AD) [12] to compute PDE derivatives. The physics-based approaches have been extended to solve complicated PDEs representing complex physics [13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]. More recently, alternate approaches that use discretization techniques using higher order derivatives and specialize numerical schemes to compute derivatives have shown to provide better regularization for faster convergence [31,32,33,34].…”

A composable autoencoder-based iterative algorithm for accelerating numerical simulations

“…Raissi et al (2019) and Raissi & Karniadakis (2018) introduced the framework of physics-informed neural network (PINN) to constrain neural networks with PDE derivatives computed using Automatic Differentiation (AD) Baydin et al (2018). In the past couple of years, the PINN framework has been extended to solve complicated PDEs representing complex physics (Jin et al, 2021;Mao et al, 2020;Rao et al, 2020;Wu et al, 2018;Qian et al, 2020;Dwivedi et al, 2021;Nabian et al, 2021;Kharazmi et al, 2021;Cai et al, 2021a;Bode et al, 2021;Taghizadeh et al, 2021;Lu et al, 2021c;Shukla et al, 2021;Hennigh et al, 2020;Li et al, 2021). More recently, alternate approaches that use discretization techniques using higher order derivatives and specialize numerical schemes to compute derivatives have shown to provide better regularization for faster convergence (Ranade et al, 2021b;Gao et al, 2021;Wandel et al, 2020;He & Pathak, 2020).…”