A rigidity-constrained sine compression-torsion
metamaterial band gap design method comprises the following steps: S100, aiming at a sine compression-torsion
metamaterial unit, establishing an analytical model among geometric parameters, equivalent rigidity and
band gap boundary frequency of the sine compression-torsion
metamaterial unit, so as to obtain a constraint equation describing the geometric parameters and the equivalent rigidity and the
band gap boundary frequency; s200, fusing the constraint equation generated in the step S100, and constructing and training a
physical information neural network for realizing high-precision and high-efficiency forward mapping from unit geometric parameters to rigidity and band gap frequency; s300, constructing and training a neural network for realizing
reverse mapping from target stiffness and band gap frequency to unit geometric parameters, and providing an initial scheme for rapid customized design; and S400, setting target rigidity and band gap conditions, and adopting an intelligent optimization
algorithm to call the trained
physical information neural
network model as a performance evaluator to perform iterative optimization. The method aims at solving the problems that in the prior art, rigidity constraint and band gap performance cannot be effectively balanced, and design and calculation efficiency is low.