90 citations · 126 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties
Nikolaos N. Vlassis, WaiChing Sun
In this paper, we introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-tuned properties. Denoising diffusion probabilistic models are generativ…
Manifold embedding data-driven mechanics
Bahador Bahmani, WaiChing Sun
This article introduces a new data-driven approach that leverages a manifold embedding generated by the invertible neural network to improve the robustness, efficiency, and accurac…
An immersed phase field fracture model for fluid-infiltrating porous media with evolving Beavers-Joseph-Saffman condition
Hyoung Suk Suh, WaiChing Sun
This study presents a phase field model for brittle fracture in fluid-infiltrating vuggy porous media. While the state-of-the-art in hydraulic phase field fracture considers Darcia…