COMMET: orders-of-magnitude speed-up in finite element method via batch-vectorized neural constitutive updates

• URL: http://arxiv.org/abs/2510.00884v1
• Date: Wed, 01 Oct 2025 13:31:56 GMT
• Title: COMMET: orders-of-magnitude speed-up in finite element method via batch-vectorized neural constitutive updates
• Abstract summary: We introduce an open-source finite element framework whose architecture has been redesigned to accelerate high-cost updates.<n>FE COMMET features a novel assembly algorithm that supports batched and vectorized evaluations, compute-graph-optimized derivatives that replace automatic differentiation, and distributed-memory parallelism via MPI.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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