Why AI Can’t See: A Physics Perspective on an Inverse Problem

1. 1Build a simple inverse model using a neural network to locate a small tumor inside a 2D domain
2. 2Use a Maxwell-based simulation to generate multistatic measurements from a circular antenna array
3. 3Analyze how symmetry and sensitivity affect the model's ability to learn and infer
4. 4Design experiments and simulations to overcome the limitations of AI models in inverse problems
5. 5Apply physics-based insights to improve the performance of AI models in computer vision and inverse problems

Data scientists and AI engineers working on computer vision and inverse problems will benefit from understanding the physics-based limitations of AI models, and how to design experiments and simulations to overcome these limitations

💡 Symmetry and sensitivity play a crucial role in shaping what a model can and cannot learn, and understanding these physics-based limitations is key to designing effective AI models

🔍 AI's ability to learn is limited by physics-based inverse problems! 🤖 Learn how symmetry and sensitivity shape what a model can and cannot learn 👉