Constitutive parameterized deep energy method for solid mechanics problems with random material parameters
CPDEM is a purely physics-driven deep learning framework to solve solid mechanics problems with random material properties without needing training data or retraining. It reformulates the energy functional by encoding material parameters into neural network inputs, allowing for zero-shot, real-time inference of displacement fields for unknown material variations.