Module autodiff 

Enhanced automatic differentiation for numerical integration

This module provides advanced automatic differentiation capabilities including:

• Forward mode AD for efficient gradient computation
• Reverse mode AD for efficient Jacobian computation
• Sparse Jacobian optimization
• Sensitivity analysis tools

Re-exports

pub use dual::Dual;

pub use dual::DualVector;

pub use forward::forward_gradient;

pub use forward::forward_jacobian;

pub use forward::ForwardAD;

pub use forward::ForwardODEJacobian;

pub use forward::VectorizedForwardAD;

pub use reverse::reverse_gradient;

pub use reverse::reverse_jacobian;

pub use reverse::CheckpointStrategy;

pub use reverse::ReverseAD;

pub use reverse::Tape;

pub use reverse::TapeNode;

pub use sensitivity::compute_sensitivities;

pub use sensitivity::MorrisScreening;

pub use sensitivity::ParameterSensitivity;

pub use sensitivity::SensitivityAnalysis;

pub use sensitivity::SobolSensitivity;

pub use sensitivity::EFAST;

pub use sparse::colored_jacobian;

pub use sparse::compress_jacobian;

pub use sparse::detect_sparsity;

pub use sparse::detect_sparsity_adaptive;

pub use sparse::BlockPattern;

pub use sparse::CSCJacobian;

pub use sparse::CSRJacobian;

pub use sparse::ColGrouping;

pub use sparse::HybridJacobian;

pub use sparse::SparseJacobian;

pub use sparse::SparseJacobianUpdater;

pub use sparse::SparsePattern;

Modules

dual

Dual number implementation for automatic differentiation

forward

Forward mode automatic differentiation

reverse

Reverse mode automatic differentiation (backpropagation)

sensitivity

Sensitivity analysis tools

sparse

Sparse Jacobian optimization