Inversion Problem

The inversion problem focuses on recovering underlying parameters from indirect, often noisy, measurements. Current research emphasizes developing efficient and robust algorithms, including neural networks (e.g., convolutional neural networks, invertible neural networks, and autoencoders) and symbolic regression methods, to solve these often ill-posed problems across diverse fields like geophysics and astronomy. These advancements aim to improve the accuracy and speed of inversions, particularly for high-dimensional datasets, while also enhancing the interpretability of results and managing computational costs. The impact spans various scientific disciplines, enabling more accurate modeling of complex systems and improved decision-making in applications ranging from resource exploration to planetary science.