METHOD AND APPARATUS FOR SOLVING COMPLEX AND COMPUTATIONALLY INTENSIVE INVERSE PROBLEMS IN REAL-TIME

摘要

The system of the present invention may 'solve' a variety of inverse physical problem types by using neural network techniques. In operation, the present invention may generate data sets characterizing a particular starting condition of a physical process (such as data sets characterizing the parameters of an initial metal die), based upon an ending condition of the physical process (such as the parameters of the metal part to be stamped by the die). In one embodiment, the system of the present invention may generate a plurality of training data sets, each training data set characterizing a sample ending condition, the physical process that results in the sample ending condition, and a sample starting condition of the physical process. The training data sets may then be applied to a neural network so as to train the network. A network definition associated with the trained neural network may be stored, and an ending data set characterizing an ending condition of the physical process may be generated. A starting data set characterizing a starting condition of the physical process may thereafter be generated based upon the stored network definition and the ending data set.