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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

8/11/2022

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:
https://onlinelibrary.wiley.com/doi/10.1002/nme.6992

Link to the cover:
https://onlinelibrary.wiley.com/doi/epdf/10.1002/nme.7087

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Our paper on phase field modeling of ice lens growth in frozen soil has been selected as the cover for Volume 46 Issue 12 of International Journal for Numerical and Analytical Methods in Geomechanics

8/9/2022

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Link to research article: onlinelibrary.wiley.com/doi/10.1002/nag.3408
Link to the cover: https://onlinelibrary.wiley.com/doi/epdf/10.1002/nag.3437

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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

Our paper on phase field modeling of ice lens growth in frozen soil has been selected as the cover for Volume 46 Issue 12 of International Journal for Numerical and Analytical Methods in Geomechanics

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