Boundary integrated neural networks (BINNs) for 2D elastostatic and piezoelectric problems: Theory and MATLAB code

In this paper, we make the first attempt to apply the boundary integrated neural networks (BINNs) for the numerical solution of two-dimensional (2D) elastostatic and piezoelectric problems. BINNs combine artificial neural networks with the well-established boundary integral equations (BIEs) to effectively solve partial differential equations (PDEs). The BIEs are utilized to map all the unknowns onto the boundary, after which these unknowns are approximated using artificial neural networks and resolved via a training process. In contrast to traditional neural network-based methods, the current BINNs offer several distinct advantages. First, by embedding BIEs into the learning procedure, BINNs only need to discretize the boundary of the solution domain, which can lead to a faster and more stable learning process (only the boundary conditions need to be fitted during the training). Second, the differential operator with respect to the PDEs is substituted by an integral operator, which effectively eliminates the need for additional differentiation of the neural networks (high-order derivatives of neural networks may lead to instability in learning). Third, the loss function of the BINNs only contains the residuals of the BIEs, as all the boundary conditions have been inherently incorporated within the formulation. Therefore, there is no necessity for employing any weighing functions, which are commonly used in traditional methods to balance the gradients among different objective functions. Moreover, BINNs possess the ability to tackle PDEs in unbounded domains since the integral representation remains valid for both bounded and unbounded domains. Extensive numerical experiments show that BINNs are much easier to train and usually give more accurate learning solutions as compared to traditional neural network-based methods

Topology optimization of broadband hyperbolic elastic metamaterials with super-resolution imaging

Hyperbolic metamaterials are strongly anisotropic artificial composite materials at a subwavelength scale and can greatly widen the engineering feasibilities for manipulation of wave propagation. However, limited by the empirical structure topologies, the previously reported hyperbolic elastic metamaterials (HEMMs) suffer from the limitations of relatively narrow frequency width, inflexible adjusting operating subwavelength scale and being difficult to further ameliorate imaging resolution. Here, we develop an inverse-design approach for HEMMs by topology optimization based on the effective medium theory. We successfully design two-dimensional broadband HEMMs supporting multipolar resonances, and theoretically demonstrate their deep-subwavelength imagings for longitudinal waves. Under different prescribed subwavelength scales, the optimized HEMMs exhibit broadband negative effective mass densities. Moreover, benefiting from the extreme enhancement of evanescent waves, an optimized HEMM at the ultra-low frequency can yield a super-high imaging resolution (~{\lambda}/64), representing the record in the field of elastic metamaterials. The proposed computational approach can be easily extended to design hyperbolic metamaterials for other wave counterparts. The present research may provide a novel design methodology for exploring the HEMMs based on unrevealed resonances and serve as a useful guide for the ultrasonography and general biomedical applications.Comment: 23 pages, 13 figure

Reducing symmetry in topology optimization of two-dimensional porous phononic crystals

In this paper we present a comprehensive study on the multi-objective optimization of two-dimensional porous phononic crystals (PnCs) in both square and triangular lattices with the reduced topology symmetry of the unit-cell. The fast non-dominated sorting-based genetic algorithm II is used to perform the optimization, and the Pareto-optimal solutions are obtained. The results demonstrate that the symmetry reduction significantly influences the optimized structures. The physical mechanism of the optimized structures is analyzed. Topology optimization combined with the symmetry reduction can discover new structures and offer new degrees of freedom to design PnC-based devices. Especially, the rotationally symmetrical structures presented here can be utilized to explore and design new chiral metamaterials.Comment: 24 pages, 11 figures in AIP Advances 201