Physics-informed neural networks for modeling rate- and temperature-dependent plasticity. (arXiv:2201.08363v1 [cond-mat.mtrl-sci])

This work presents a physics-informed neural network based framework to model
the strain-rate and temperature dependence of the deformation fields
(displacement, stress, plastic strain) in elastic-viscoplastic solids. A
detailed discussion on the construction of the physics-based loss criterion
along with a brief outline on ways to avoid unbalanced back-propagated
gradients during training is also presented. We also present a simple strategy
with no added computational complexity for choosing scalar weights that balance
the interplay between different terms in the composite …

arxiv modeling networks neural networks physics rate