Live briefing
Everything about Adjoint state method
Numerical method
The adjoint state method is a numerical method for efficiently computing the gradient of a function or operator in a numerical optimization problem. It has applications in geophysics, seismic imaging, photonics and more recently in neural networks. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Adjoint state methodA 5-minute orientation
- Skim a recent paper: Topography‐Incorporated Adjoint‐State Surface Wave Traveltime Tomography: Method and a Case Study in HawaiiInfluential work since 2022
- Pick up a classic book: The assistant principalThe most-reprinted book on the subject
- Read the landmark paper: First-arrival traveltime tomography based on the adjoint-state methodCited 238 times
- Go sideways: Proximal gradient methods for learningA closely related topic
- Go sideways: Ricci solitonA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
First-arrival traveltime tomography based on the adjoint-state method
An adjoint state method for three-dimensional transmission traveltime tomography using first-arrivals
Slope tomography based on eikonal solvers and the adjoint-state method
Parsimonious slope tomography based on eikonal solvers and the adjoint-state method
First-arrival traveltime tomography for anisotropic media using the adjoint-state method
State and parameter estimation in 1-D hyperbolic PDEs based on an adjoint method
A level-set adjoint-state method for crosswell transmission-reflection traveltime tomography
Adjoint Method for Missile Performance Analysis on State-Space Models
🆕 Influential since 2022
Topography‐Incorporated Adjoint‐State Surface Wave Traveltime Tomography: Method and a Case Study in Hawaii
Adjoint‐State Teleseismic Traveltime Tomography: Method and Application to Thailand in Indochina Peninsula
Preconditioned transmission + reflection joint traveltime tomography with adjoint‐state method for subsurface velocity model building
Automatic design of interpretable control laws through parametrized Genetic Programming with adjoint state method gradient evaluation
High-precision and high-efficiency first-arrival slope tomography via eikonal solvers and the adjoint-state method
📚 Books worth reading
The assistant principalL. David Weller, Sylvia J. Weller · 2001
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Proximal gradient methods for learning Proximal gradient (forward backward splitting) methods for learning is an area of research in optimization and statistical learning theory which studies…
- Ricci soliton In differential geometry, a complete Riemannian manifold ( M , g ) {\displaystyle (M,g)} is called a Ricci soliton if, and only if, there exists a smooth…
- Fictitious domain method In numerical analysis, the fictitious domain method (FD) is a numerical technique designed to solve partial differential equations on complex geometries…
- Position operator In quantum mechanics, the position operator is the operator that corresponds to the position observable of a particle. When the position operator is considered…
- Lattice model (physics) In mathematical physics, a lattice model is a mathematical model of a physical system that is defined on a lattice, as opposed to a continuum, such as…
- Casson invariant In 3-dimensional topology, a part of the mathematical field of geometric topology, the Casson invariant is an integer-valued invariant of oriented integral…
- Proximal operator In mathematical optimization, the proximal operator is an operator associated with a proper, lower semi-continuous convex function f {\displaystyle f}…
- Tau function (integrable systems) Tau functions are an important ingredient in the modern mathematical theory of integrable systems, and have numerous applications in a variety of other…