Differentiable Programming

Differentiable programming (DP) is a programming paradigm that enables the computation of gradients through complex programs, facilitating gradient-based optimization of program parameters. Current research focuses on applying DP to diverse fields, including scientific computing (solving partial differential equations, simulating physical systems), biology (modeling morphogenesis), and engineering (optimal control, robot design), often integrating DP with machine learning models like neural networks. This approach allows for the seamless integration of physical laws and data-driven methods, leading to more accurate, efficient, and robust solutions in various scientific and engineering domains.