SYSTEM DESIGN BASED ON PROCESS FLOW DIAGRAM INFORMATION EXTRACTION AND GENERATIVE MODELS
One embodiment provides a method and a system for automated design of a physical system. During operation, the design system obtains qualitative and quantitative design requirements associated with the physical system and inputs the qualitative design requirements to a trained machine-learning model to generate a topology of the physical system. The topology specifies a number of components and connections among the components. The design system then determines parameters of the components based on the quantitative design requirements.
1 . A computer-implemented method for automated design of a physical system, the method comprising:
obtaining qualitative and quantitative design requirements associated with the physical system;
inputting the qualitative design requirements to a trained machine-learning model to generate a topology of the physical system, wherein the topology specifies a number of components and connections among the components; and
determining parameters of the components based on the quantitative design requirements.
2 . The method of claim 1 , wherein the machine-learning model is a generative model.
3 . The method of claim 2 , further comprising training the generative model, which comprises:
obtaining images of a plurality of process flow diagrams (PFDs) associated with known systems;
extracting, from the images, topology information associated with the known systems; and
converting the topology information into a predetermined format.
4 . The method of claim 3 , wherein extracting the topology information comprises applying an image-processing technique to detect components and connections among the detected components within each image.
5 . The method of claim 3 , wherein training the generative model further comprises associating a label with the converted topology information from each PFD, wherein the label comprises a functional description of a system corresponding to the PFD.
6 . The method of claim 3 , wherein the predetermined format comprises a formal language representation of the topology information.
7 . The method of claim 6 , wherein the formal language representation comprises a number of component-connection sequences, wherein a respective component-connection sequence comprises a statement indicating a connection order of a number of components.
8 . The method of claim 2 , wherein the generative model comprises a natural language processing (NLP) machine-learning model comprising one or more of:
an N-gram language model;
a recurrent neural net (RNN) language model;
a hidden Markov model;
a model implementing probabilistic context-free grammars;
a naïve Bayes model;
a latent Dirichlet allocation (LDA) model;
a sequence to sequence (Seq2Seq) model; and
a transformer model.
9 . The method of claim 1 , wherein determining the parameters of the components comprises using an optimization technique to search a parameter space associated with a respective component.
10 . The method of claim 9 , further comprising:
in response to failing to find parameters of the components meeting the quantitative design requirements, generating, by the trained machine-learning model, an additional topology of the physical system.
11 . A computer system for automated design of a physical system, the computer system comprising:
a processor; and
a storage device coupled to the processor and storing instructions, which when executed by the processor cause the processor to perform a method, the method comprising:
obtaining qualitative and quantitative design requirements associated with the physical system;
inputting the qualitative design requirements to a trained machine-learning model to generate a topology of the physical system, wherein the topology specifies a number of components and connections among the components; and
determining parameters of the components based on the quantitative design requirements.
12 . The computer system of claim 11 , wherein the machine-learning model is a generative model.
13 . The computer system of claim 12 , wherein the method further comprises training the generative model, which comprises:
obtaining images of a plurality of process flow diagrams (PFDs) associated with known systems;
extracting, from the images, topology information associated with the known systems; and
converting the topology information into a predetermined format.
14 . The computer system of claim 13 , wherein extracting the topology information comprises applying an image-processing technique to detect components and connections among the detected components within each image.
15 . The computer system of claim 13 , wherein training the generative model further comprises associating a label with the converted topology information from each PFD, wherein the label comprises a functional description of a system corresponding to the PFD.
16 . The computer system of claim 13 , wherein the predetermined format comprises a formal language representation of the topology information.
17 . The computer system of claim 16 , wherein the formal language representation comprises a number of component-connection sequences, wherein a respective component-connection sequence comprises a statement indicating a connection order of a number of components.
18 . The computer system of claim 12 , wherein the generative model comprises a natural language processing (NLP) machine-learning model comprising one or more of:
an N-gram language model;
a recurrent neural net (RNN) language model;
a hidden Markov model;
a model implementing probabilistic context-free grammars;
a naïve Bayes model;
a latent Dirichlet allocation (LDA) model;
a sequence to sequence (Seq2Seq) model; and
a transformer model.
19 . The computer system of claim 11 , wherein determining the parameters of the components comprises using an optimization technique to search a parameter space associated with a respective component.
20 . The computer system of claim 19 , wherein the method further comprises:
in response to failing to find parameters of the components meeting the quantitative design requirements, generating, by the trained machine-learning model, an additional topology of the physical system.