We introduce a computational approach to optimize random porous materials via parametric growth processes. We focus on the problem of minimizing the Poisson’s ratio of a two-phase porous random material, which results in an auxetic material. Initially, we perform a parametric optimization of the growth process. Afterward, the optimized parametric growth process implicitly generates an auxetic random material. Namely, the growth process intrinsically entails the formation of an auxetic material. Our approach enables the computation of large-scale auxetic random materials in commodity computers. We also provide numerical results indicating that the computed auxetic materials have close to isotropic linear elastic behavior and physical tests revealing auxetic behavior.

我们引入了一种计算方法，通过参数化生长过程优化随机多孔材料。我们专注于最小化两相多孔随机材料的泊松比的问题，这会导致拉胀材料。最初，我们对生长过程进行参数优化。之后，优化的参数增长过程隐含地生成拉胀随机材料。即，生长过程本质上需要形成拉胀材料。我们的方法能够在商用计算机中计算大规模拉胀随机材料。我们还提供了数值结果，表明计算出的拉胀材料具有接近各向同性的线弹性行为和揭示拉胀行为的物理测试。