Global adjoint tomography: first-generation model

By Ebru Bozdag, Daniel Peter, Matthieu Lefebvre, Dimitri Komatitsch, Jeroen Tromp, Judith C. Hill, Norbert Podhorszki, D. Pugmire. 2016

247 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Geophysics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/f9b289ff09230a5837c5cd2ea1f3d0272144567c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Jeroen Tromp
2. G. Em Karniadakis
3. Dimitri Komatitsch
4. D. May

In Geophysics

1. Andreas Fichtner
2. Ebru Bozdag
3. Jeroen Tromp
4. Dimitri Komatitsch
5. Qinya Liu
6. Daniel Peter
7. D. Pugmire
8. Norbert Podhorszki

In Radiology, Nuclear Medicine and Imaging

1. G. Em Karniadakis

Citations in the corpus

• Global adjoint tomography—model GLAD-M25 (2020, methodology): “0 as a result of the 15 iterations performed for GLAD-M15), especially in view of the significant expense of the source inversions.”
• Physics‐Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions (2021, methodology): “However, the derivation and implementation of adjoint methods for FWIs remains challenging and must be treated on a case-by-case basis for different systems (Bozdağ et al., 2016; Cockett et al., 2015).”
• Modular and flexible spectral-element waveform modelling in two and three dimensions (2018, methodology): “This point is especially relevant when considering full-waveform inversion, which has its place amongst the largest PDE-constrained optimization problems (Afanasiev et al. 2016; Bozdag et al. 2016).”