Machine learning methods have attracted growing interest across many fields, including solid mechanics. Constitutive artificial neural networks (CANNs) have shown high efficiency and accuracy for modeling hyperelastic materials, while physics-informed neural networks (PINNs) provide a data-free alternative to conventional simulation techniques. However, standard PINNs often require large, complex networks and dense sampling in the simulation […]