We present a new approach for predictive modeling and its uncertainty quantification for mechanical systems, where coarse-grained models such as constitutive relations are derived directly from observation data. We explore the use of a neural network to represent the unknown constitutive relations, compare the neural networks with piecewise linear functions, radial basis functions, and radial basis function networks, and show that the neural network outperforms the others in certain cases. We analyze the approximation error of the neural networks using a scaling argument. The training and predicting processes in our framework combine the finite element method, automatic differentiation, and neural networks (or other function approximators). Our framework also allows uncertainty quantification in the form of confidence intervals. Numerical examples on a multiscale fiber-reinforced plate problem and a nonlinear rubbery membrane problem from solid mechanics demonstrate the effectiveness of our framework.

Summary Embedded Boundary Methods (EBMs) are often preferred for the solution of Fluid‐Structure Interaction (FSI) problems because they are reliable for large structural motions/deformations and topological changes. For viscous flow problems, however, they do not track the boundary layers that form around embedded obstacles and therefore do not maintain them resolved. Hence, an Adaptive Mesh Refinement (AMR) framework for EBMs is proposed in this paper. It is based on computing the distance from an edge of the embedding computational fluid dynamics mesh to the nearest embedded discrete surface and on satisfying the y+ requirements. It is also equipped with a Hessian‐based criterion for resolving flow features such as shocks, vortices, and wakes and with load balancing for achieving parallel efficiency. It performs mesh refinement using a parallel version of the newest vertex bisection method to maintain mesh conformity. Hence, while it is sufficiently comprehensive to support many discretization methods, it is particularly attractive for vertex‐centered finite volume schemes where dual cells tend to complicate the mesh adaptation process. Using the EBM known as FIVER, this AMR framework is verified for several academic FSI problems. Its potential for realistic FSI applications is also demonstrated with the simulation of a challenging supersonic parachute inflation dynamics problem.

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