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==Advantages and disadvantages==
===Advantages===
Sources:<ref name="Han2018" /><ref name="Beck2019" /> # High-Dimensional Capability: Compared to traditional numerical methods, deep BSDE performs exceptionally well in high-dimensional problems.
# Flexibility: The incorporation of deep neural networks allows this method to adapt to various types of BSDEs and financial models.
# Parallel Computing: Deep learning frameworks support GPU acceleration, significantly improving computational efficiency.
===Disadvantages===
Sources:<ref name="Han2018" /><ref name="Beck2019" /> # Training Time: Training deep neural networks typically requires substantial data and computational resources.
# Parameter Sensitivity: The choice of neural network architecture and hyperparameters greatly impacts the results, often requiring experience and trial-and-error.
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