自动微分框架可从位移或应力数据中发现材料模型，支持物理、数据驱动及混合模型。
Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency
- 基于全场位移与全局力数据，实现历史依赖材料模型的自动微分更新。
- 可适配从几十到数百万参数的多种模型，支持局部与全局两种发现方式。
- 开源工具HookeAI集成高效批处理计算图，免调参且易扩展，适合材料建模研究者。
我们提出首个自动微分模型更新框架ADiMU，能够从全场位移和全局力数据（全局、间接发现）或应变-应力数据（局部、直接发现）中，自动发现任意历史依赖的材料模型。该框架可更新传统（物理基础）、神经网络（数据驱动）及混合型材料模型。无需额外调参或引入新变量，仅依赖用户选定模型架构和优化器的固有参数。通过全可微代码实现，算法利用向量化映射，在共享计算图上高效批量执行，确保历史依赖自动微分的稳定性。本工作还致力于推动未来材料模型架构的集成、评估与应用，因此公开发布为开源工具，并集成于设计严谨、文档完善的软件HookeAI中。
原文摘要 · Abstract (English)
We introduce the first Automatically Differentiable Model Updating (ADiMU) framework that finds any history-dependent material model from full-field displacement and global force data (global, indirect discovery) or from strain-stress data (local, direct discovery). We show that ADiMU can update conventional (physics-based), neural network (data-driven), and hybrid material models. Moreover, this framework requires no fine-tuning of hyperparameters or additional quantities beyond those inherent to the user-selected material model architecture and optimizer. The robustness and versatility of ADiMU is extensively exemplified by updating different models spanning tens to millions of parameters, in both local and global discovery settings. Relying on fully differentiable code, the algorithmic implementation leverages vectorizing maps that enable history-dependent automatic differentiation via efficient batched execution of shared computation graphs. This contribution also aims to facilitate the integration, evaluation and application of future material model architectures by openly supporting the research community. Therefore, ADiMU is released as an open-source computational tool, integrated into a carefully designed and documented software named HookeAI.
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