Releases: DIG-Kaust/MDD-StochasticSolvers
Releases · DIG-Kaust/MDD-StochasticSolvers
IEEE Submission
Matteo refactoring
Code refractored and new examples for Salt synthetic and Volve data. Currently step-size is chosen empirically.
EAGE submission
The current version of the code is used to produce results for EAGE 2022 submission.
A series of improvements have been made compared to the previous release:
- Step-size is chosen using the Landweber step
- New examples on Volve-like synthetic data
- New pure numpy implementation (benchmarked with the original torch implementation)
End of Tamil's internship
vTamil Updated the file