82 citations · 92 across the 8 of their papers we have counts for
3 papers · 1 filter
Mixture of Polyconvex Neural Potentials for Parametric Hyperelasticity: Towards Foundation Material Models
Steven J. Yang, Govinda Anantha Padmanabha, D. Thomas Seidl +1
Hyperelastic constitutive models enable modeling large deformations in elastic solids. In common practice, a strain energy density function is prescribed in advance and model-speci…
Interval and fuzzy physics-augmented neural networks (iPANN and fPANN) for uncertainty quantification and propagation in constitutive modeling
Somesh Pratap Singh, Govinda Anantha Padmanabha, Jingye Tan +4
Constitutive modeling under uncertainty remains a central challenge for reliable mechanics simulations, particularly when the available stress-deformation data are sparse, noisy, o…
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU)
Jingye Tan, Govinda Anantha Padmanabha, Steven J. Yang +1
Recent progress in AI-enabled constitutive modeling has concentrated on moving from a purely data-driven paradigm to the enforcement of physical constraints and mechanistic princip…