optframework: Optimization-Based Framework for Kernel Parameter Identification in Multi-Material Population Balance Models
Repo: On Github (https://github.com/adin888/PSD_opt)
Authors: Frank Rhein (frank.rhein@kit.edu), Haoran Ji (haoran.ji@kit.edu).
Affiliation: Karlsruhe Institute of Technology (KIT), Institute of Mechanical Process Engineering and Mechanics
This project provides Python-based solvers and optimizers for the Population Balance Equation (PBE).
Depending on the application, users can choose the most suitable solver to predict the evolution of particle systems.
For cases with experimental data, the optimization framework can be used to determine kernel parameters that best match experimental conditions, improving prediction accuracy.
See Quick Start for simple usage examples of the solvers.
Visit Overview to understand the overall project structure.
For a full list of class attributes and their meanings, refer to Attributes.
The documentation also includes two advanced guides:
Contents:
- Quick Start
- Overview
- Attributes
- Advanced Guide: Adding a Custom Kernel Model
- Advanced Guide: Coupling Custom Solvers with the Optimizer
- References
- optframework package
- Subpackages
- optframework.base package
- optframework.dpbe package
- optframework.examples package
- Subpackages
- Submodules
- optframework.kernel_opt package
- Submodules
- optframework.kernel_opt.opt_algo_bo module
- optframework.kernel_opt.opt_base module
- optframework.kernel_opt.opt_base_ray module
- optframework.kernel_opt.opt_core module
- optframework.kernel_opt.opt_core_multi module
- optframework.kernel_opt.opt_core_multi_ray module
- optframework.kernel_opt.opt_core_ray module
- optframework.kernel_opt.opt_data module
- optframework.kernel_opt.opt_pbe module
- Submodules
- optframework.mcpbe package
- optframework.pbm package
- optframework.utils package
- optframework.validation package
- Subpackages