Adjoint Method

The adjoint method is a powerful technique for efficiently computing gradients of objective functions with respect to model parameters, particularly useful in complex systems governed by differential equations. Current research focuses on applying the adjoint method to diverse areas, including neural networks (e.g., improving backpropagation, training neural ODEs), image processing (e.g., dehazing), and data-driven discovery of partial differential equations. This approach offers significant advantages in optimization problems, enabling faster training of machine learning models and more efficient solutions to inverse problems across various scientific and engineering disciplines.