Tutorial: OccamyPy, an object-oriented optimization framework for large-scale inverse problems скачать в хорошем качестве

Tutorial: OccamyPy, an object-oriented optimization framework for large-scale inverse problems

Francesco Picetti & Ettore Biondi What you'll need: Slack channel: #t22-tue-occamypy (visit https://softwareunderground.org/slack to join) https://github.com/fpicetti/occamypy We present a python library that can be employed to solve small- and large-scale problems based on the concept of vectors and operators. Based on NumPy, CuPy and PyTorch, it includes different iterative optimization algorithms that can be used in combination with architecture-independent vectors and operators, thus running on CPU, GPUs and HPC clusters with a unique codebase. Inspired by PyLops, we demonstrate its flexibility and scalability on multiple inverse problems, where convex and non-convex objective functions are optimized with different iterative algorithms. TIME STAMPS 00:00:00 Start Streaming 0:05 Transform 2022 information 2:15 Intro 4:00 Why building such a library 4:56 Which language? Python! 5:12 General concepts and library structure 9:54 Examples 18:37 Simple Quadratic problems notebook 32:50 SplitBregman Examples 52:30 Torch example 56:56 PyLop and OccamPy together 1:04:33 Dask-based classes 1:13:40 2D LS-RTM with devito, dask and regularizers 1:33:35 Traveltime Eikonal Tomography 2:03:00 Conclusions