Adjoint state method is a well-known method to efficiently compute the gradient of a cost or objective function for a simulation-driven optimization problem. Essentially, it computes the adjoint action of Born operator (the linearized forward map) on any given vector. This report presents a derivation of adjoint state algorithm for an acoustic system discretized by staggered grid finite difference schemes, and discusses its implementation based on the modeling package IWAVE. Our goal is to construct a C++ wrapper of IWAVE, which fits into a general framework for inversion. This report is the second of several describing an implementation of such a wrapper
Knowing how the solution to time-harmonic wave scattering problems depends on medium properties and ...
The circuit tuning problem is best approached by means of gradient-based nonlinear optimization algo...
Many problems in physics and modern computing are inverse problems -- problems where the desired out...
Combination of object-oriented programming with automatic differentiation techniques facilitates the...
The C++ class fdtd uses automatic differentiation techniques to implement an abstract time stepping ...
Adaptive grids in inverse and control problems can lead to computed objective functions that are non...
To solve seismic inverse problems via the adjoint state method, we must be able to repeatedly solve ...
Abstract — Recently the concept of optimal boundary control by adjoint modelling has been introduced...
Adjoint methods are a key ingredient of gradient-based full-waveform inversion schemes. While being ...
International audienceFull Waveform Inversion (FWI) is becoming an efficient tool to derive high res...
The determination of optimal geometric arrangements and electronic drives of loudspeaker arrays in s...
We present in this paper the computation of the DSO objective function in the general acoustic case....
This paper presents a number of algorithm developments for adjoint methods using the `discrete&apos...
The adjoint-state method is widely used for computing gradients in simulation-driven optimization pr...
This paper presents a number of algorithm developments for adjoint meth-ods using the `discrete &apo...
Knowing how the solution to time-harmonic wave scattering problems depends on medium properties and ...
The circuit tuning problem is best approached by means of gradient-based nonlinear optimization algo...
Many problems in physics and modern computing are inverse problems -- problems where the desired out...
Combination of object-oriented programming with automatic differentiation techniques facilitates the...
The C++ class fdtd uses automatic differentiation techniques to implement an abstract time stepping ...
Adaptive grids in inverse and control problems can lead to computed objective functions that are non...
To solve seismic inverse problems via the adjoint state method, we must be able to repeatedly solve ...
Abstract — Recently the concept of optimal boundary control by adjoint modelling has been introduced...
Adjoint methods are a key ingredient of gradient-based full-waveform inversion schemes. While being ...
International audienceFull Waveform Inversion (FWI) is becoming an efficient tool to derive high res...
The determination of optimal geometric arrangements and electronic drives of loudspeaker arrays in s...
We present in this paper the computation of the DSO objective function in the general acoustic case....
This paper presents a number of algorithm developments for adjoint methods using the `discrete&apos...
The adjoint-state method is widely used for computing gradients in simulation-driven optimization pr...
This paper presents a number of algorithm developments for adjoint meth-ods using the `discrete &apo...
Knowing how the solution to time-harmonic wave scattering problems depends on medium properties and ...
The circuit tuning problem is best approached by means of gradient-based nonlinear optimization algo...
Many problems in physics and modern computing are inverse problems -- problems where the desired out...