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Found 5 papers, 2 papers with code
Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks
A proper statistical evaluation of the results underlines the high accuracy of the deterministic calibration and that the estimated uncertainty is valid.
Physics-Informed Neural Networks for Material Model Calibration from Full-Field Displacement Data
In the current work, we propose PINNs for the calibration of constitutive models from full-field displacement and global force data in a realistic regime on the example of linear elasticity.
Spiking neural networks for nonlinear regression
To overcome this problem, a framework for regression using spiking neural networks is proposed.
Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics
The lightweight algorithm is able to learn the underlying properties of the material from a single microCT-scan without the need of explicit descriptors.
Physics informed neural networks for continuum micromechanics
Recently, physics informed neural networks have successfully been applied to a broad variety of problems in applied mathematics and engineering.
