Direct inference of viscoelastic memory from chirp rheometry via physics-informed Gaussian processes
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.