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Learning finite viscoelasticity with DAVIS: A supervised framework for generalized standard materials
Simon Wiesheier, Paul Steinmann, Miguel Angel Moreno-Mateos
This work revisits the recently proposed data-adaptive viscoelasticity (DAVIS) framework, a spline-based formulation of finite viscoelasticity within the generalized standard mater…
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…
Data-adaptive spline surfaces for non-separable hyperelastic energy functions
Simon Wiesheier, Miguel Angel Moreno-Mateos, Paul Steinmann
Invariant-based models for incompressible isotropic hyperelasticity are typically formulated as functions of the first and second invariants, . A widel…
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…