Neural Inverse

Neural inverse methods aim to solve inverse problems—determining underlying causes from observed effects—by leveraging the power of neural networks. Current research focuses on developing robust architectures, such as those combining deep operator networks and physics-informed neural networks, to handle complex mappings between operators and functions, and incorporating uncertainty quantification for improved reliability. These techniques find applications across diverse fields, from material design and photonic device engineering to image processing and solving partial differential equations, offering faster and more accurate solutions than traditional methods. The ability to verify the correctness of these neural inverse models is also a growing area of focus, enhancing their trustworthiness in safety-critical applications.