Investigating the performance of Deep Material Networks in accelerating multiscale modelling of laminated composites

Modern material systems with properly designed microstructures offer new avenues for engineering materials with advantageous mechanical properties and functionalities in various applications. With the increasing desire to design structures that have complex shapes or that are simply cheaper and more efficient, the demand for complex numerical...

On leveraging physical knowledge to augment neural network-based surrogate models for simulations

The computational cost of high-fidelity engineering simulations, for example CFD, is prohibitive if the application requires frequent design iterations or even fully fledged optimization. A popular way to reduce the computational cost and enable fast iteration cycles is to use surrogate models that are trained to predict simulation results from...