AI 中文总结
该研究提出物理信息高斯过程框架，从啁啾流变原始时域记录直接推断粘弹性记忆核，无需中间信号处理，可预测未见过的变形响应，解析材料演化，提升软材料表征效率。
AI 中文摘要
软材料会记忆其变形历史，从实验中识别该记忆对于预测这些材料在实际载荷条件下的行为至关重要。啁啾流变测量近年来成为加速该表征的一种方法，可将数小时的常规测量压缩至数秒，每次实验产生数千对应力-应变数据。但该数据密度大多被浪费：标准流程会在拟合本构模型前，将记录简化为少数频域估计值。我们提出一种物理信息高斯过程框架，可直接从单次啁啾的原始时域记录中推断材料的本构定律，在候选记忆核中进行选择并对所选核进行参数化，无需任何中间信号处理步骤。由于该框架推断的是记忆核而非训练期间使用的特定波形，因此无需重新训练即可预测其未见过的变形历史的响应，还能直接在时域中解析单次啁啾内的材料演化。
英文摘要
Soft materials remember their deformation history, and identifying that memory from experiments is essential for predicting how these materials behave under real-world loading conditions. Chirp rheometry has recently emerged as a way to accelerate this characterization, compressing hours of conventional measurement into seconds and yielding thousands of stress-strain pairs per experiment. That density is then largely discarded: the standard pipeline reduces the record to a handful of frequency-domain estimates before any constitutive model is fitted. We introduce a physics-informed Gaussian process framework that infers the material's constitutive law directly from the raw time-domain record of a single chirp, selecting among candidate memory kernels and parametrizing the selected one without any intermediate signal processing step. Because the framework infers the memory kernel rather than the specific waveform used during training, it predicts the response to deformation histories it never saw, without retraining. The method also resolves material evolution within a single chirp directly in the time domain.
