Mahindra Rautela - Material identification

Deep generation of material properties and representations using deep latent space models

AI-accelerated property prediction, discovery, and design of materials.
The generator can generate enormous data, eliminating the demerits of both experiments and simulations.
With our proposed approach, the expensive simulator can be replaced with a deep generator and a deep property predictor.
The proposed approach can be helpful in accelerating and scaling the design of lightweight impact-resistant structures.
More about the research is available here.

Deep material property estimation using inverse deep surrogate models

This research work is published in Mechanics of Advanced Materials & Structures and at 2 conferences. Check it out here, here, and here.

Motivation:

Characterization of composite materials is necessary for non-destructive property measurements, and real-time material degradation aspects.

General problem statement:

Inverse problem statement:

Solution schemes:

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