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Correlative image learning of chemo-mechanics in phase-transforming solids
[article]
2021
arXiv
pre-print
Constitutive laws underlie most physical processes in nature. However, learning such equations in heterogeneous solids (e.g., due to phase separation) is challenging. One such relationship is between composition and eigenstrain, which governs the chemo-mechanical expansion in solids. In this work, we developed a generalizable, physically-constrained image-learning framework to algorithmically learn the chemo-mechanical constitutive law at the nanoscale from correlative four-dimensional scanning
arXiv:2107.06192v1
fatcat:6i62lx3bsjdjngj33lcqmqyuwu