Abstracts

Machine learning (ML)-based materials models have shown great promise in capturing constitutive relations for material systems where history and microstructure dependencies are too complex for closed-form analytical or empirical models. Due to the robust ecosystem of ML frameworks, particularly in Python, new models can…