Inversion Method

Inversion methods aim to reconstruct underlying properties from indirect measurements, a crucial task across diverse scientific fields. Current research focuses on improving inversion accuracy and robustness, particularly using deep learning architectures like generative adversarial networks (GANs) and diffusion models, often coupled with regularization techniques to mitigate ill-posedness and noise. These advancements are significantly impacting various applications, from geophysical imaging and medical diagnostics to image editing and the solution of inverse problems in partial differential equations, by enabling faster, more accurate, and data-efficient reconstructions. The integration of physical models with data-driven approaches is a particularly active area of development.