Scattered Light

Scattered light analysis aims to extract information about an object's internal structure or composition from the way it scatters light or other electromagnetic waves. Current research heavily utilizes machine learning, particularly convolutional neural networks (like ResNet50) and neural fields, to efficiently solve the inverse scattering problem—reconstructing the object's properties from scattered light patterns. These techniques are improving the resolution and speed of tomographic imaging in diverse fields, including medical imaging (e.g., brain tissue analysis) and remote sensing, by overcoming limitations of traditional methods. The development of physics-informed neural networks further enhances accuracy and computational efficiency.