ML for engineering and science applications.
A Keras wrapper for scientific computations and physics-informed deep learning using artificial neural networks
A vectorized FEM MATLAB library using the implicit interface methods
Use of deep learning in engineering analysis and inversion.
A proposal for a recommendation system for learning taste profile (3rd place winner).
Contact description, friction (Coulomb and Rate-and-State), and contact search.
Constitutive modeling of isotropic and anisotropic materials including nonlinear elasticity, plasticity, and heterogeneity.
Cohesive fraction propagation, localization and shear band formation, and hydraulic fracking.
FEM, XFEM, Meshfree, FVM, IGA, etc.