Model Based Deep Learning

Model-based deep learning integrates physical models with deep neural networks to solve inverse problems, leveraging the strengths of both approaches for improved accuracy and efficiency. Current research focuses on enhancing robustness to model mismatches, developing efficient architectures like deep equilibrium models and unrolled networks, and applying these methods to diverse fields such as medical imaging, wireless communications, and material science. This interdisciplinary approach promises significant advancements by enabling faster, more accurate, and more interpretable solutions to complex problems across various scientific and engineering domains.