Classical numerical methods such as finite element method (FEM) face limitations due to
their low efficiency when addressing large-scale problems. As a novel paradigm, the physics …

Physics-informed neural networks (PINNs) are well-regarded for their capabilities in inverse
analysis. However, efficient convergence is hard to achieve due to the necessity of …

This work introduces a hybrid approach that combines the Proper Generalised
Decomposition (PGD) with deep learning techniques to provide real-time solutions for …

In this review, we explore the development of the structural constitutive models for soft
biological tissues and biomaterials, focusing on the role of mechanical interactions. Soft …

Computational models that can predict growth and remodeling of the heart could have
important clinical applications. However, the time it takes to calibrate and run current models …