This invention provides an AI-adaptive finite element mesh and integral optimization method,
system, medium, and device, belonging to the field of
finite element simulation technology. The method includes: dividing the target region into an initial mesh and setting integration points; performing a preliminary
simulation to obtain the
physical field distribution; extracting
physical field gradient data; constructing an input
feature set by combining geotechnical medium parameters, mesh, and
integration point information; training an AI optimization model using a
physical information neural network to generate a dynamically adjusted scheme; and after performing a secondary
simulation, re-inputting the updated gradient data into the AI model, iteratively optimizing based on the topology change rate and
physical field error until convergence. This invention aligns with geotechnical
mechanics and seepage laws, avoids purely data-driven biases, significantly reduces redundant calculations while ensuring computational accuracy, and significantly improves the
simulation efficiency and accuracy of physical fields such as stress, strain, and seepage. It is suitable for complex geological and nonlinear working conditions and has a high level of
automation and intelligence.