Modelling operations on functional structures

A computer-implemented method comprising:

• obtaining a generative autoencoder configured to generate functional structures, a functional structure being a data structure representing a mechanical assembly of rigid parts, the data structure including a tree which includes:
• leaf nodes each representing a shape and positioning of a respective rigid part and a force exerted on the respective rigid part,
• non-leaf nodes each with several children and each representing a mechanical link between sub-assemblies, each sub-assembly being represented by a respective one of the several children, and
• non-leaf nodes with a single child and each representing a duplication of the sub-assembly represented by the single child;
• and
• using the generative autoencoder,
• wherein the shape and positioning of the respective rigid part is represented by data including a specification of a bounding box around the respective rigid part, and the force exerted on the respective rigid part is a resulting force, and
• wherein the generative autoencoder includes:
• an encoder having a leaf encoding module, a mechanical link encoding module, and a duplication encoding module, and
• a decoder including a node classifier, a leaf decoding module, a mechanical link decoding module, and a duplication decoding module,
• wherein the encoder is configured, with respect to a given functional structure, to encode the tree into a single latent vector according to a recursive bottom-up process where:
• the leaf encoding module outputs, for each leaf node, a respective latent vector, based on the data comprising the specification of the bounding box and the resulting force,
• the mechanical link encoding module outputs, for each non-leaf node representing a mechanical link, a respective latent vector, based on several latent vectors which encode the respective several children of the non-leaf node, and
• the duplication encoding module outputs, for each non-leaf node representing a duplication, a respective latent vector, based on the latent vector which encodes the respective single child of the non-leaf node, and
• wherein the decoder is configured, with respect to a given single latent vector, to decode the given single latent vector into a tree of a functional structure according to a recursive top-down process where:
• the node classifier classifies each latent vector produced during the decoding, as corresponding to a leaf node, to a non-leaf node representing a mechanical link, or to a non-leaf node representing a duplication,
• the leaf decoding module outputs, for each latent vector classified as corresponding to a leaf node during the decoding, data comprising a specification of a respective bounding box and a respective resulting force, based on the latent vector,
• the mechanical link decoding module outputs, for each latent vector classified as corresponding to a non-leaf node representing a mechanical link during the decoding, several child latent vectors, based on the latent vector, and
• the duplication decoding module outputs, for each latent vector classified as corresponding to a non-leaf node representing a duplication during the decoding, a single child latent vector, based on the latent vector.

A device comprising:

• a non-transitory data storage medium having recorded thereon a computer program comprising instructions for teaching a generative autoencoder that generates functional structures, a functional structure being a data structure representing a mechanical assembly of rigid parts, the data structure including a tree which includes leaf nodes each representing a shape and positioning of a respective rigid part and a force exerted on the respective rigid part, non-leaf nodes each with several children and each representing a mechanical link between sub-assemblies, each sub-assembly being represented by a respective one of the several children, and non-leaf nodes with a single child and each representing a duplication of the sub-assembly represented by the single child, the computer program when executed by a processor causes the processor to be configured to:
• obtaining a dataset including functional structures, and
• teach the generative autoencoder on the dataset,
• wherein the shape and positioning of the respective rigid part is represented by data including a specification of a bounding box around the respective rigid part, and the force exerted on the respective rigid part is a resulting force, and
• wherein the generative autoencoder includes:
• an encoder having a leaf encoding module, a mechanical link encoding module, and a duplication encoding module, and
• a decoder including a node classifier, a leaf decoding module, a mechanical link decoding module, and a duplication decoding module,
• wherein the encoder is configured, with respect to a given functional structure, to encode the tree into a single latent vector according to a recursive bottom-up process where:
• the leaf encoding module outputs, for each leaf node, a respective latent vector, based on the data comprising the specification of the bounding box and the resulting force,
• the mechanical link encoding module outputs, for each non-leaf node representing a mechanical link, a respective latent vector, based on several latent vectors which encode the respective several children of the non-leaf node, and
• the duplication encoding module outputs, for each non-leaf node representing a duplication, a respective latent vector, based on the latent vector which encodes the respective single child of the non-leaf node, and
• wherein the decoder is configured, with respect to a given single latent vector, to decode the given single latent vector into a tree of a functional structure according to a recursive top-down process where:
• the node classifier classifies each latent vector produced during the decoding, as corresponding to a leaf node, to a non-leaf node representing a mechanical link, or to a non-leaf node representing a duplication,
• the leaf decoding module outputs, for each latent vector classified as corresponding to a leaf node during the decoding, data comprising a specification of a respective bounding box and a respective resulting force, based on the latent vector,
• the mechanical link decoding module outputs, for each latent vector classified as corresponding to a non-leaf node representing a mechanical link during the decoding, several child latent vectors, based on the latent vector, and
• the duplication decoding module outputs, for each latent vector classified as corresponding to a non-leaf node representing a duplication during the decoding, a single child latent vector, based on the latent vector.

A device comprising

• a non-transitory data storage medium having recorded thereon a computer program comprising instructions that when executed by a processor causes the processor to be configured to:
• obtain a generative autoencoder configured to generate functional structures, a functional structure being a data structure representing a mechanical assembly of rigid parts, the data structure including a tree which includes:
• leaf nodes each representing a shape and positioning of a respective rigid part and a force exerted on the respective rigid part,
• non-leaf nodes each with several children and each representing a mechanical link between sub-assemblies, each sub-assembly being represented by a respective one of the several children, and
• non-leaf nodes with a single child and each representing a duplication of the sub-assembly represented by the single child;
• and
• using the generative autoencoder,
• wherein the shape and positioning of the respective rigid part is represented by data including a specification of a bounding box around the respective rigid part, and the force exerted on the respective rigid part is a resulting force, and
• wherein the generative autoencoder includes:
• an encoder having a leaf encoding module, a mechanical link encoding module, and a duplication encoding module, and
• a decoder including a node classifier, a leaf decoding module, a mechanical link decoding module, and a duplication decoding module,
• wherein the encoder is configured, with respect to a given functional structure, to encode the tree into a single latent vector according to a recursive bottom-up process where:
• the leaf encoding module outputs, for each leaf node, a respective latent vector, based on the data comprising the specification of the bounding box and the resulting force,
• the mechanical link encoding module outputs, for each non-leaf node representing a mechanical link, a respective latent vector, based on several latent vectors which encode the respective several children of the non-leaf node, and
• the duplication encoding module outputs, for each non-leaf node representing a duplication, a respective latent vector, based on the latent vector which encodes the respective single child of the non-leaf node, and
• wherein the decoder is configured, with respect to a given single latent vector, to decode the given single latent vector into a tree of a functional structure according to a recursive top-down process where:
• the node classifier classifies each latent vector produced during the decoding, as corresponding to a leaf node, to a non-leaf node representing a mechanical link, or to a non-leaf node representing a duplication,
• the leaf decoding module outputs, for each latent vector classified as corresponding to a leaf node during the decoding, data comprising a specification of a respective bounding box and a respective resulting force, based on the latent vector,
• the mechanical link decoding module outputs, for each latent vector classified as corresponding to a non-leaf node representing a mechanical link during the decoding, several child latent vectors, based on the latent vector, and
• the duplication decoding module outputs, for each latent vector classified as corresponding to a non-leaf node representing a duplication during the decoding, a single child latent vector, based on the latent vector.

A device comprising:

• a non-transitory data storage medium having recorded thereon a generative autoencoder configured to generate functional structures, a functional structure being a data structure representing a mechanical assembly of rigid parts, the data structure including a tree which includes:
• leaf nodes each representing a shape and positioning of a respective rigid part and a force exerted on the respective rigid part;
• non-leaf nodes each with several children and each representing a mechanical link between sub-assemblies, each sub-assembly being represented by a respective one of the several children, and
• non-leaf nodes with a single child and each representing a duplication of the sub-assembly represented by the single child,
• wherein the shape and positioning of the respective rigid part is represented by data including a specification of a bounding box around the respective rigid part, and the force exerted on the respective rigid part is a resulting force, and
• wherein the generative autoencoder includes:
• an encoder having a leaf encoding module, a mechanical link encoding module, and a duplication encoding module, and
• a decoder including a node classifier, a leaf decoding module, a mechanical link decoding module, and a duplication decoding module,
• wherein the encoder is configured, with respect to a given functional structure, to encode the tree into a single latent vector according to a recursive bottom-up process where:
• the leaf encoding module outputs, for each leaf node, a respective latent vector, based on the data comprising the specification of the bounding box and the resulting force,
• the mechanical link encoding module outputs, for each non-leaf node representing a mechanical link, a respective latent vector, based on several latent vectors which encode the respective several children of the non-leaf node, and
• the duplication encoding module outputs, for each non-leaf node representing a duplication, a respective latent vector, based on the latent vector which encodes the respective single child of the non-leaf node, and
• wherein the decoder is configured, with respect to a given single latent vector, to decode the given single latent vector into a tree of a functional structure according to a recursive top-down process where:
• the node classifier classifies each latent vector produced during the decoding, as corresponding to a leaf node, to a non-leaf node representing a mechanical link, or to a non-leaf node representing a duplication,
• the leaf decoding module outputs, for each latent vector classified as corresponding to a leaf node during the decoding, data comprising a specification of a respective bounding box and a respective resulting force, based on the latent vector,
• the mechanical link decoding module outputs, for each latent vector classified as corresponding to a non-leaf node representing a mechanical link during the decoding, several child latent vectors, based on the latent vector, and
• the duplication decoding module outputs, for each latent vector classified as corresponding to a non-leaf node representing a duplication during the decoding, a single child latent vector, based on the latent vector.