Direct numerical simulation of hierarchical materials via homogenization-based concurrent multiscale models poses critical challenges for 3D large scale engineering applications, as the computation of highly nonlinear and path-dependent material constitutive responses at the lower scale causes prohibitively high computational costs. In this work, we propose a physics-informed data-driven deep learning model as an efficient surrogate to emulate the effective responses of heterogeneous microstructures under irreversible elasto-plastic hardening and softening deformation. Our contribution contains several major innovations. First, we propose a novel training scheme to generate arbitrary loading sequences in the sampling space confined by deformation constraints where the simulation cost of homogenizing microstructural responses per sequence is dramatically reduced via mechanistic reduced-order models. Second, we develop a new sequential learner that incorporates thermodynamics consistent physics constraints by customizing training loss function and data flow architecture. We additionally demonstrate the integration of trained surrogate within the framework of classic multiscale finite element solver. Our numerical experiments indicate that our model shows a significant accuracy improvement over pure data-driven emulator and a dramatic efficiency boost than reduced models. We believe our data-driven model provides a computationally efficient and mechanics consistent alternative for classic constitutive laws beneficial for potential high-throughput simulations that needs material homogenization of irreversible behaviors.
翻译:通过基于同质的同质性同时的多尺度模型,对等级材料进行直接数字模拟,对3D大型工程应用提出了重大挑战,因为计算高度非线性和基于路径的低尺度材料构成反应,导致过高的计算成本。在这项工作中,我们提议以物理知情数据驱动的深层次学习模型作为有效替代,以效仿在不可逆的极光塑料硬化和软化变形下的各种微型结构的有效反应。我们的贡献包含若干重大创新。首先,我们提议了一个新的培训计划,以便在取样空间产生任意的装载序列,这种排解限制限制了采样空间的容积,因为通过机械性减序模型使同质微结构反应的模拟成本大大降低。第二,我们开发了一个新的顺序学习器,通过对培训损失功能和数据流结构进行定制,将热力一致的物理限制纳入其中。我们进一步展示了经过培训的超标准多尺度元素解析器框架的集成。我们的数字实验表明,我们的模型显示,在纯数据驱动的模化模版模化模化和急剧效率提升了比降低的模型的急剧提高效率,我们相信,能够进行一个稳定的模型。