Treffer: XAD Automatic Differentiation Library

AD is a comprehensive open-source C++ library for automatic differentiation by Xcelerit. It targets production-quality code at any scale, striving for both ease of use and high performance. Key features: Forward and adjoint mode for any order, using operator-overloading Checkpointing support (for tape memory management) External functions interface (to integrate external libraries) Thread-safe tape Formal exception-safety guarantees High performance Battle-tested in large production code bases Python bindings Application areas: Machine Learning and Deep Learning: Training neural networks or other machine learning models. Optimization: Solving optimization problems in engineering and finance. Numerical Analysis: Enhancing numerical solution methods for differential equations. Scientific Computing: Simulating physical systems and processes. Risk Management and Quantitative Finance: Assessing and hedging risk in financial models. Computer Graphics: Optimizing rendering algorithms. Robotics: Improving control and simulation of robotic systems. Meteorology: Enhancing weather prediction models. Biotechnology: Modeling biological processes and systems. It is available from: GitHub repository Documentation