14 citations · 25 across the 5 of their papers we have counts for
5 papers
Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functions
Karl A. Kalina, Jörg Brummund, WaiChing Sun +1
We present a data-driven framework for the multiscale modeling of anisotropic finite strain elasticity based on physics-augmented neural networks (PANNs). Our approach allows the e…
A review on data-driven constitutive laws for solids
Jan Niklas Fuhg, Govinda Anantha Padmanabha, Nikolaos Bouklas +6
This review article highlights state-of-the-art data-driven techniques to discover, encode, surrogate, or emulate constitutive laws that describe the path-independent and path-depe…
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter
Ruben Villarreal, Nikolaos N. Vlassis, Nhon N. Phan +5
Experimental data is costly to obtain, which makes it difficult to calibrate complex models. For many models an experimental design that produces the best calibration given a limit…
Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings
Bahador Bahmani, WaiChing Sun
This paper presents a PINN training framework that employs (1) pre-training steps that accelerates and improve the robustness of the training of physics-informed neural network wit…
Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation
Xiao Sun, Bahador Bahmani, Nikolaos N. Vlassis +2
This paper presents a computational framework that generates ensemble predictive mechanics models with uncertainty quantification (UQ). We first develop a causal discovery algorith…