Inverse Problems
Summary
Inverse problems involve determining hidden system parameters from measurements, where the forward process is well-defined but the inverse process may be ambiguous. Invertible Neural Networks (INNs) have emerged as a promising approach to tackle these challenges. Unlike traditional neural networks that attempt to solve the inverse problem directly, INNs learn the forward and inverse processes jointly, using latent output variables to capture information that would otherwise be lost. This allows INNs to provide a full distribution over parameter space for a given measurement, making them particularly useful for analyzing multi-modalities, uncovering parameter correlations, and identifying unrecoverable parameters. The effectiveness of INNs has been demonstrated in various fields, including astrophysics and medicine, showcasing their potential as a powerful tool for solving complex inverse problems in natural sciences and beyond.