Projects #
DeepCallCenter #
This project is an implementation of the paper:
authored by Baris Ata and Ebru Kasilarlar.
The paper discusses a dynamic scheduling problem for a call center system. The problem is modeled as a CTMDP, which yields a brownian control problem at the heavy-traffic limit. The paper shows that the brownian control problem can be solved using a deep learning approach, and with that, good control policies can be found for the CTMDP.
Note: This project depends on two of my previous projects:
TorchSimulator #
TorchSimulator is a demonstration framework designed to showcase how parallel, tensorized simulations on GPUs can accelerate performance. It provides a foundation for building custom simulation classes by allowing users to inherit from the provided CTMCSimulator or CTMDPSimulator classes.
In the tutorial.ipynb provided, we walked through a dynamic scheduling problem for a queueing system, where each sample path spans 17 days (approximately 20,000 events).
While simulating 10,000 sample paths takes about 10 minutes on CPU (using C++), GPU acceleration enables running 1,000,000 paths within 15 minutes. This is a 60x speedup.
TorchBSDE Solver Package #
This repository implements
Jiequn Han’s
DeepBSDE solver in PyTorch. This solver
solves high-dimensional PDEs using deep learning.
The code has been restructured to work either as an installable Python package or as a git submodule in other projects. Extensive comments and docstrings have been added to enhance readability and understanding of the implementation.
DeepBSDE Solver Package in TensorFlow (2.0) #
This repository is a fork of Jiequn Han’s DeepBSDE solver implementation, which solves high-dimensional PDEs using deep learning.
The code has been restructured to work either as an installable Python package or as a git submodule in other projects. While changes to the original code are minimal, extensive comments and docstrings have been added to enhance readability and understanding of the implementation.