jerome-cong / deepbsde

Deep BSDE solver in TensorFlow

Deep BSDE Solver in TensorFlow/Pytorch

Usage

• I recommend doing installation in a virtual environment such as Anaconda, Python 3.9 is used in this implementation.
• Install PyTorch

conda install cudatoolkit=11.8
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

• Clone this repository

git clone https://github.com/Jerome-Cong/DeepBSDE.git
pip3 install -e .

Training

• With TensorFlow:

python main.py --config_path=configs/hjb_lq_d100.json

• With PyTorch:

python main_torch.py --config_path=configs/hjb_lq_d100.json

Command-line flags:

• config_path: Config path corresponding to the partial differential equation (PDE) to solve. There are seven PDEs implemented so far. See Problems section below.
• exp_name: Name of numerical experiment, prefix of logging and output.
• log_dir: Directory to write logging and output array.

Problems

equation.py and config.py now support the following problems:

Three examples in ref [1]:

• HJBLQ: Hamilton-Jacobi-Bellman (HJB) equation.
• AllenCahn: Allen-Cahn equation with a cubic nonlinearity.
• PricingDefaultRisk: Nonlinear Black-Scholes equation with default risk in consideration.

Four examples in ref [2]:

• PricingDiffRate: Nonlinear Black-Scholes equation for the pricing of European financial derivatives with different interest rates for borrowing and lending.
• BurgersType: Multidimensional Burgers-type PDEs with explicit solution.
• QuadraticGradient: An example PDE with quadratically growing derivatives and an explicit solution.
• ReactionDiffusion: Time-dependent reaction-diffusion-type example PDE with oscillating explicit solutions.

New problems can be added very easily. Inherit the class equation in equation.py and define the new problem. Note that the generator function and terminal function should be TensorFlow operations while the sample function can be python operation. A proper config is needed as well.

Reference

[1] Han, J., Jentzen, A., and E, W. Overcoming the curse of dimensionality: Solving high-dimensional partial differential equations using deep learning, Proceedings of the National Academy of Sciences, 115(34), 8505-8510 (2018). [journal] [arXiv]
[2] E, W., Han, J., and Jentzen, A. Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations, Communications in Mathematics and Statistics, 5, 349–380 (2017). [journal] [arXiv]