Patent ID: 11966674
Assignee: TATA CONSULTANCY SERVICES LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 12:
13. One or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause managing a plurality of events, the instructions cause:
receiving a set of requirements from a user based on an use case, wherein the set of requirements comprises one or more of a set of dimensional parameters, field profiles, or field resonances, wherein the set of dimension parameters refers to geometries of the artificially structured materials;
creating a dataset corresponding to a structure of the artificially structured materials using the set of requirements;
training a forward model using the dataset, wherein the set of dimensional parameters are provided as input and predicting their corresponding electromagnetic response as output using a coupled artificial neural network and sequence model based architecture, wherein the network is forced to learn the geometries of meta materials devices;
treating EM spectrum data as a time varying sequence and inverse problem as a single-input, multi-output (SIMO) model, wherein the coupled artificial neural network and sequence model based architecture learns geometry of designs from the trained forward model;
saving weights and biases for the forward model;
fixing the weights and the biases of a decoder part in an encoder-decoder tandem network from the saved weights and the biases of the forward model;
training the encoder-decoder tandem network for an inverse problem using a single input multiple output model with the electromagnetic response as input and an output electromagnetic response at a time distributed layer of the encoder-decoder tandem network in the end and the dimension of artificially structured materials at a mid-dense layer of the encoder-decoder tandem network as output;
saving the weights and the biases of the encoder part from the trained encoder-decoder tandem network;
providing the electromagnetic response as input to the trained encoder-decoder tandem network to obtain a final set of dimensions from the mid-dense layer of the encoder-decoder tandem network; and
designing, via the one or more hardware processors, the artificially structured material using the final set of dimensions.