83 citations · 122 across the 11 of their papers we have counts for
12 papers
Brilliance: toward autonomous computational research in particle accelerator physics
Jinyu Wan, Yu Zhao, Wei Bao +4
Computational research in physics requires continuous interplay between simulation and scientific interpretation, demanding substantial specialist effort. Here we introduce Brillia…
Artificial Intelligence and the Generative Science of Food Formulation
Vahidullah Tac, Ellen Kuhl
Food formulation requires balancing taste, nutrition, sustainability, and cost. Traditionally, new foods have emerged through empirical experimentation, expert intuition, and itera…
Generative AI for material design: A mechanics perspective from burgers to matter
Vahidullah Tac, Ellen Kuhl
Generative artificial intelligence offers a new paradigm to design matter in high-dimensional spaces. However, its underlying mechanisms remain difficult to interpret and limit ado…
Watching Physics: the Generative Science of Matter and Motion
Hagen Holthusen, Kevin Linka, Ellen Kuhl
Can we learn the physics of matter in motion directly from images and video--and trust it? Answering this question requires integrating experiments, physics-based simulation, and d…
Learning ultra-compressible hyperelasticity with splines: Constitutive asymmetries and non-unique representations
Miguel Angel Moreno-Mateos, Simon Wiesheier, Paul Steinmann +1
Highly compressible solids, such as foams, exhibit complex responses, including pronounced tension-compression asymmetry. Capturing such behaviors within unified hyperelastic frame…
A Convex Route to Thermoelasticity: Learning Internal Energy and Dissipation
Hagen Holthusen, Paul Steinmann, Ellen Kuhl
We present a physics-based neural network framework for the discovery of constitutive models in fully coupled thermomechanics. In contrast to classical formulations based on the He…
A Complement to Neural Networks for Anisotropic Inelasticity at Finite Strains
Hagen Holthusen, Ellen Kuhl
We propose a complement to constitutive modeling that augments neural networks with material principles to capture anisotropy and inelasticity at finite strains. The key element is…
Generalized invariants meet constitutive neural networks: A novel framework for hyperelastic materials
Denisa Martonová, Alain Goriely, Ellen Kuhl
The major challenge in determining a hyperelastic model for a given material is the choice of invariants and the selection how the strain energy function depends functionally on th…
Autoencoder-based non-intrusive model order reduction in continuum mechanics
Jannick Kehls, Ellen Kuhl, Tim Brepols +2
We propose a non-intrusive, Autoencoder-based framework for reduced-order modeling in continuum mechanics. Our method integrates three stages: (i) an unsupervised Autoencoder compr…
A generalized dual potential for inelastic Constitutive Artificial Neural Networks: A JAX implementation at finite strains
Hagen Holthusen, Kevin Linka, Ellen Kuhl +1
We present a methodology for designing a generalized dual potential, or pseudo potential, for inelastic Constitutive Artificial Neural Networks (iCANNs). This potential, expressed…
Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling
Hagen Holthusen, Tim Brepols, Kevin Linka +1
Soft biological tissues exhibit a tendency to maintain a preferred state of tensile stress, known as tensional homeostasis, which is restored even after external mechanical stimuli…
Theory and implementation of inelastic Constitutive Artificial Neural Networks
Hagen Holthusen, Lukas Lamm, Tim Brepols +2
Nature has always been our inspiration in the research, design and development of materials and has driven us to gain a deep understanding of the mechanisms that characterize aniso…