90 citations · 305 across the 20 of their papers we have counts for
8 papers · 1 filter
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…
Viscoelasticty with physics-augmented neural networks: Model formulation and training methods without prescribed internal variables
Max Rosenkranz, Karl A. Kalina, Jörg Brummund +2
We present an approach for the data-driven modeling of nonlinear viscoelastic materials at small strains which is based on physics-augmented neural networks (NNs) and requires only…
Physics-constrained symbolic model discovery for polyconvex incompressible hyperelastic materials
Bahador Bahmani, WaiChing Sun
We present a machine learning framework capable of consistently inferring mathematical expressions of hyperelastic energy functionals for incompressible materials from sparse exper…
Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria
Karl A. Kalina, Philipp Gebhart, Jörg Brummund +3
We present a framework for the multiscale modeling of finite strain magneto-elasticity based on physics-augmented neural networks (NNs). By using a set of problem specific invarian…
Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions
Bahador Bahmani, Hyoung Suk Suh, WaiChing Sun
Conventional neural network elastoplasticity models are often perceived as lacking interpretability. This paper introduces a two-step machine learning approach that returns mathema…
Synthesizing realistic sand assemblies with denoising diffusion in latent space
Nikolaos N. Vlassis, WaiChing Sun, Khalid A. Alshibli +1
The shapes and morphological features of grains in sand assemblies have far-reaching implications in many engineering applications, such as geotechnical engineering, computer anima…
MD-inferred neural network monoclinic finite-strain hyperelasticity models for -HMX: Sobolev training and validation against physical constraints
Nikolaos N. Vlassis, Puhan Zhao, Ran Ma +2
We present a machine learning framework to train and validate neural networks to predict the anisotropic elastic response of the monoclinic organic molecular crystal -HMX in the…