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

Claim 8:
9. A system for designing artificially structured materials, the system comprises:
an input/output interface configured to receive 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;
one or more hardware processors; and
a memory in communication with the one or more hardware processors, wherein the one or more first hardware processors are configured to execute programmed instructions stored in the memory, to
create a dataset corresponding to a structure of the artificially structured materials using the set of requirements;
train 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 the meta materials devices;
treat 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;
save weights and biases for the forward model;
fix 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 to control a combined loss;
train 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;
save the weights and the biases of the encoder part from the trained encoder-decoder tandem network;
fix the weights and the biases of the encoder from the saved weights and the biases of the trained encoder-decoder tandem network;
provide 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
design the artificially structured material using the final set of dimensions.