1. Benchmarking physics-informed frameworks for data-driven hyperelasticity;V Ta�;Computational Mechanics,2023
2. A new family of constitutive artificial neural networks towards automated model discovery;K Linka;Computer Methods in Applied Mechanics and Engineering,2023
3. On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling;J N Fuhg;Computer Methods in Applied Mechanics and Engineering,2022
4. Geometric deep learning for computational mechanics part i: anisotropic hyperelasticity;N N Vlassis;Computer Methods in Applied Mechanics and Engineering,2020
5. Recent advances and applications of machine learning in experimental solid mechanics: A review;H Jin;Applied Mechanics Reviews,2023