Chapter 14. Scientific Computing Applications

Differential equations are one of the main reasons automatic differentiation matters in scientific computing. Many scientific models are not written as closed-form functions....

9 items

Section	Title
1	Chapter 14. Scientific Computing Applications
2	Sensitivity Analysis
3	Inverse Problems
4	Computational Fluid Dynamics
5	Molecular Simulation
6	Computational Finance
7	Signal Processing
8	Robotics and Control
9	Probabilistic Programming

Writes › Book › Auto Diff › Chapter 14. Scientific Computing Applications ›

Chapter 14. Scientific Computing Applications

Differential equations are one of the main reasons automatic differentiation matters in scientific computing. Many scientific models are not written as closed-form functions....

Writes › Book › Auto Diff › Chapter 14. Scientific Computing Applications ›

Sensitivity Analysis

Sensitivity analysis studies how changes in inputs affect the outputs of a system. In differential equations, optimization, simulation, and machine learning, the main object...

Writes › Book › Auto Diff › Chapter 14. Scientific Computing Applications ›

Inverse Problems

An inverse problem asks for causes from effects. A forward model predicts observations from parameters. An inverse model tries to recover parameters from observations.

Writes › Book › Auto Diff › Chapter 14. Scientific Computing Applications ›

Computational Fluid Dynamics

Computational fluid dynamics studies fluid motion by solving discretized forms of the governing equations. Automatic differentiation enters CFD when we want gradients of...

Writes › Book › Auto Diff › Chapter 14. Scientific Computing Applications ›

Molecular Simulation

Molecular simulation models the behavior of atoms and molecules using physical interaction laws. Automatic differentiation is important because many molecular methods require...

Writes › Book › Auto Diff › Chapter 14. Scientific Computing Applications ›

Computational Finance

Computational finance uses numerical models to price contracts, measure risk, and optimize portfolios. Automatic differentiation is useful because most financial computations...

Writes › Book › Auto Diff › Chapter 14. Scientific Computing Applications ›

Signal Processing

Signal processing studies how information is represented, transformed, filtered, compressed, reconstructed, and estimated from signals. A signal may be a time series, an...

Writes › Book › Auto Diff › Chapter 14. Scientific Computing Applications ›

Robotics and Control

Robotics and control systems interact with the physical world through sensing, estimation, planning, and actuation. Automatic differentiation is important because modern...

Writes › Book › Auto Diff › Chapter 14. Scientific Computing Applications ›

Probabilistic Programming

Probabilistic programming represents uncertainty using executable probabilistic models. A probabilistic program defines a distribution rather than only a deterministic computation.