Deep Tech R&D: AI for Advanced Signal Processing
Completed:
This research and development project focused on creating a defensible technological asset by moving beyond standard AI applications. The goal was to build a “deep tech” solution with potential applications in sectors like aerospace, industrial manufacturing, and smart infrastructure.
The Challenge
Conventional signal processing methods for noise cancellation often fail in complex, dynamic environments. The challenge was to design an AI model that could understand the underlying physics of a space to actively negate noise with higher efficacy.
Solution
A physics-informed neural network (PINN) in PyTorch, that unlike traditional models, integrates physical laws into its learning process, allowing it to adapt and perform robustly in real-world conditions.
Tech & Skills
- Languages: Python
- Frameworks: PyTorch
- Core Competencies: Deep Learning, R&D, Physics-Informed AI, Signal Processing