python main.py --config_path=configs/hjb_lq_d100.jsonCommand-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.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.
Note: an old version of the deep BSDE solver compatiable with TensorFlow 1.12 and Python 2 can be found in the commit 9d4e332.
[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]