5 papers
Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models
Chenyi Ji, Kian P. Abdolazizi, Hagen Holthusen +2
A key problem of solid mechanics is the identification of the constitutive law of a material, that is, the relation between strain history and stress. Machine learning has lead to…
Electromechanical computational model of the human stomach
Maire S. Henke, Sebastian Brandstaeter, Sebastian L. Fuchs +3
The stomach plays a central role in digestion through coordinated muscle contractions, known as gastric peristalsis, driven by slow-wave electrophysiology. Understanding this proce…
Machine-learned domain partitioning for computationally efficient coupling of continuum and particle simulations of membrane fabrication
Matthias Busch, Gregor Häfner, Jiayu Xie +4
All simulation approaches eventually face limits in computational scalability when applied to large spatiotemporal domains. This challenge becomes especially apparent in molecular-…
A 3D-1D-0D Multiscale Model of the Neuro-Glial-Vascular Unit for Synaptic and Vascular Dynamics in the Dorsal Vagal Complex
Alexander Hermann, Tobias Köppl, Andreas Wagner +5
Cerebral blood flow regulation is critical for brain function, and its disruption is implicated in various neurological disorders. Many existing models do not fully capture the com…
Constitutive Kolmogorov-Arnold Networks (CKANs): Combining Accuracy and Interpretability in Data-Driven Material Modeling
Kian P. Abdolazizi, Roland C. Aydin, Christian J. Cyron +1
Hybrid constitutive modeling integrates two complementary approaches for describing and predicting a material's mechanical behavior: purely data-driven black-box methods and physic…