A physics-augmented neural network learns composition-dependent, rate-dependent stress-strain behavior of PolyJet digital materials, interpolating well to held-out blends but extrapolating poorly.
(1998) 3D printing with metals.Computing and Control Engineering Journal 9, 31–38
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials
A physics-augmented neural network learns composition-dependent, rate-dependent stress-strain behavior of PolyJet digital materials, interpolating well to held-out blends but extrapolating poorly.