This application provides a nonlinear finite element accelerated
solution system and method. The
system includes: a
physical information neural
network model construction module configured to: acquire configuration points in the solution domain of the nonlinear
partial differential equation to be solved; input the configuration points into a fully connected neural network to obtain a predicted solution; and obtain a
physical information neural
network model based on the predicted solution; an input module configured to: construct a finite
element model of the nonlinear
partial differential equation to be solved; transform the nonlinear
partial differential equation to be solved into a
system of nonlinear algebraic equations; and generate a predicted value vector based on the finite
element model; and a solution module configured to: solve the
system of nonlinear algebraic equations using a nonlinear
solver to obtain the target solution. This addresses the problem that in current finite element methods for parameterized research scenarios, each time a new parameter value is input, a complete and computationally expensive nonlinear
finite element solution process needs to be started independently, leading to repetitive calculations and low efficiency.