Our paper on the two-step data-driven/physics-constrained machine learning model to predict finite sterain elasticity of energetic materials has been selected as the September cover of IJNME
In this work, we first use MD data to train a deep neural network to create the "first draft" of the continuum surrogate model, then use well known physics constraints from continuum mechanics (e.g. material frame indifference, material symmetry, growth condition, rank-one convexity) to further fine-tune the neural network. The resultant framework is used to predict the finite strain elasticity of Nitroamine high explosive (HMX), an application that requires robustness and consistency on the forecast quality.
Link to research article:
Link to the cover:
News about Computational Poromechanics lab at Columbia University.