GENERATIVE NETWORKS FOR PHYSICS BASED SIMULATIONS

Simulations of products during the design of the products can use solvers that are based on trained neural networks, and these solvers can provide results about the design of the product that can predict performance, failures, fatigue and other potential problems with the design. The neural network can include a generative neural network that is trained with a discretized version of a partial differential equation (PDE) that provides a model of the product in the simulation, and this discretized version acts as a discriminator that trains the neural network to provide solutions to the PDE.