Constitutive evaluations often dominate the computational cost of finite element (FE) simulations whenever material models are complex. Neural constitutive models (NCMs) offer a highly expressive and flexible framework for modeling complex material behavior in solid mechanics. However, their practical adoption in large-scale FE simulations remains limited due to significant computational costs, especially in repeatedly evaluating stress and stiffness. NCMs thus represent an extreme case: their large computational graphs make stress and stiffness evaluations prohibitively expensive, restricting their use to small-scale problems. In this work, we introduce COMMET, an open-source FE framework whose architecture has been redesigned from the ground up to accelerate high-cost constitutive updates. Our framework features a novel assembly algorithm that supports batched and vectorized constitutive evaluations, compute-graph-optimized derivatives that replace automatic differentiation, and distributed-memory parallelism via MPI. These advances dramatically reduce runtime, with speed-ups exceeding three orders of magnitude relative to traditional non-vectorized automatic differentiation-based implementations. While we demonstrate these gains primarily for NCMs, the same principles apply broadly wherever for-loop based assembly or constitutive updates limit performance, establishing a new standard for large-scale, high-fidelity simulations in computational mechanics.

翻译：在有限元（FE）仿真中，每当材料模型变得复杂时，本构评估往往主导计算成本。神经本构模型（NCMs）为固体力学中复杂材料行为的建模提供了一个高度表达且灵活的框架。然而，由于显著的计算成本，尤其是在反复评估应力和刚度时，其在大规模有限元仿真中的实际应用仍然受限。因此，NCMs代表了一种极端情况：其庞大的计算图使得应力和刚度评估成本过高，限制了其在小规模问题中的使用。在这项工作中，我们介绍了COMMET，这是一个开源的有限元框架，其架构经过彻底重新设计，旨在加速高成本的本构更新。我们的框架具有一种新颖的组装算法，支持批量化和向量化的本构评估；采用计算图优化的导数来替代自动微分；并通过MPI实现分布式内存并行。这些进步显著减少了运行时间，相对于传统的非向量化基于自动微分的实现，加速超过三个数量级。虽然我们主要针对NCMs展示了这些性能提升，但同样的原理广泛适用于任何基于循环的组装或本构更新限制性能的场景，从而为计算力学中的大规模、高保真仿真确立了新标准。