System for Machine Learning-Based Acceleration of a Topology Optimization Process

A system for accelerating topology optimization of a design, comprising:

• a topology optimization module configured to compute state variables of the topology using a two-scale topology optimization for a number of optimization steps using design variables mapped to a fine-scale mesh and the state variables mapped to a coarse-scale mesh, wherein the state variables are computed using finite element analysis based on a simulated load and boundary conditions on the objective design and are accumulated with corresponding design variables as history data;
• a machine learning module comprising a machine learning-based model having a tunable number of hidden layers configured to:
• execute an initial training of the machine learning-based model using the history data for a first number of optimization steps (WI);
• determine a predicted sensitivity value related to the design variables using the trained machine learning-based model for each of a second number of optimization steps (NF);
• execute an online update of the machine learning-based model using updated history data for a third number of optimization steps (WU);
• update the design variables based on the predicted sensitivity value for each optimization step;
• and
• recursively repeat the optimization steps until the updated design variables are within a tolerance of prior updated design variables;
• wherein the topology optimization module executes the two-scale optimization only prior to and during the first number of optimization steps (WI) that generate the history data for the initial training of the machine learning-based model and during optimization steps for a duration of the third number of steps (WU) initiated periodically at an update frequency equal to the second number of optimization steps (NF) for generating the updated history data.