US 6,208,982 B1Granted

Method and apparatus for solving complex and computationally intensive inverse problems in real-time

AssigneeLockheed Martin Energy Research Corporation

InventorsJohn D. Allen, Jr., Nenad Ivezic, Gerard M. Ludtka, Thomas Zacharia

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Abstract

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.

Claims9

Dependent claims

A system for generating parameters characterizing a first physical object, wherein the first physical object is adapted according to a specified transformation process so as to modify a second physical object, the system comprising:

(a) means for generating a plurality of sets of training parameters, each set of training parameters characterizing:
(i) a sample second physical object,
(ii) the specified transformation process that results in the sample second physical object, and
(iii) a sample first physical object that interacts with the specified transformation process so as to modify the sample second physical object;
(b) means for operating a neural network;
(c) means for applying the plurality of sets of training parameters to the neural network so as to train the neural network, wherein each of the plurality of sets of training parameters is processed by the applying means over a plurality of patches, each patch comprising a data array, two orthogonal axes crossing the data array and a plurality of regions along the axes, so as to provide at least one single output node by averaging over the plurality of regions without losing information contained in the data array, thereby producing a network definition associated with the trained neural network;
(d) means for storing the network definition;
(e) means for generating a set of second parameters characterizing the second physical object;
and
(f) processing means for generating a set of first parameters characterizing the first physical object based upon the set of second parameters and the network definition stored by the storing means.

7 more elements

Dependencies are inferred from the claim text.