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{{Artificial intelligence|Approaches}}
[[File:Deep backward stochastic differential equation method.png|thumb|Neural Network Framework of Deep BSDE Method<ref name="Han2018">{{cite journal | last1=Han | first1=J. | last2=Jentzen | first2=A. | last3=E | first3=W. | title=Solving high-dimensional partial differential equations using deep learning | journal=Proceedings of the National Academy of Sciences | volume=115 | issue=34 | pages=8505-8510 | year=2018 }}</ref>]]{{Artificial intelligence|Approaches}}▼
{{differential equations}}
'''Deep BSDE''' ('''Deep Backward Stochastic Differential Equation''') is a numerical method that combines [[deep learning]] with [[Backward stochastic differential equation]] (BSDE). This method is particularly useful for solving high-dimensional problems in [[financial derivatives]] pricing and [[risk management]]. By leveraging the powerful function approximation capabilities of [[deep neural networks]], deep BSDE addresses the computational challenges faced by traditional numerical methods in high-dimensional settings <ref name="Han2018">{{cite journal | last1=Han | first1=J. | last2=Jentzen | first2=A. | last3=E | first3=W. | title=Solving high-dimensional partial differential equations using deep learning | journal=Proceedings of the National Academy of Sciences | volume=115 | issue=34 | pages=8505-8510 | year=2018 }}</ref>.
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===Neural network architecture<ref name="Han2018" />===
▲[[File:Deep backward stochastic differential equation method.png|thumb|Neural Network Framework of Deep BSDE Method<ref name="Han2018">{{cite journal | last1=Han | first1=J. | last2=Jentzen | first2=A. | last3=E | first3=W. | title=Solving high-dimensional partial differential equations using deep learning | journal=Proceedings of the National Academy of Sciences | volume=115 | issue=34 | pages=8505-8510 | year=2018 }}</ref>]]
Deep learning encompass a class of machine learning techniques that have transformed numerous fields by enabling the modeling and interpretation of intricate data structures. These methods, often referred to as [[deep learning]], are distinguished by their hierarchical architecture comprising multiple layers of interconnected nodes, or neurons. This architecture allows deep neural networks to autonomously learn abstract representations of data, making them particularly effective in tasks such as [[image recognition]], [[natural language processing]], and [[financial modeling]]. The core of this method lies in designing an appropriate neural network structure (such as [[fully connected network|fully connected networks]] or [[recurrent neural networks]]) and selecting effective optimization algorithms<ref>LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. *Nature, 521*(7553), 436-444.</ref>.
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