Method and system for a generative artificial intelligence based sensor design synthesis of metamaterial

A system comprising:

• a memory storing instructions;
• one or more Input/Output (I/O) interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to:
• receive one or more geometrical structures with a plurality of geometric design parameters, and one or more optical responses as a target sensor response to synthesize a metamaterial sensor design using a generative artificial intelligence (GenAI) model;
• generate a set of optical response conditioned on the received one or more geometrical structures in a forward path learning technique using a conditional variational auto-encoder, wherein the conditional variational auto-encoder is conditioned by the received one or more geometrical structure along with sensitivity relationship (SenR);
• and
• generate a geometrical structure of the metamaterial from the set of optical response in an inverse path learning technique using the conditional variational auto-encoder, wherein the conditional variational auto-encoder is conditioned on optical response along with the sensitivity relationship (SenR) and with one or more domain adaptable features.

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:

• receiving, via an input/output (I/O) interface, one or more geometrical structures with a plurality of geometric design parameters, and one or more optical responses as a target sensor response to synthesize a metamaterial sensor design using a generative artificial intelligence (GenAI) model;
• generating a set of optical response conditioned on the received one or more geometrical structures in a forward path learning technique using a conditional variational auto-encoder, wherein the conditional variational auto-encoder is conditioned by the received one or more geometrical structures along with sensitivity relationship (SenR);
• and
• generating a geometrical structure of the metamaterial from the set of optical response in an inverse path learning technique using the conditional variational auto-encoder, wherein the conditional variational auto-encoder is conditioned on optical response along with the sensitivity relationship (SenR) and with one or more domain adaptable features.