arXiv (All)
open access
A Deep BSDE Method for a Class of Strongly Coupled FBSDEs
We investigate a variant of the deep BSDE method introduced by E et al. (2017, 2018). The key novelty is that we establish an a-posteriori convergence result for the approximation of strongly coupled forward-backward stochastic differential equations (FBSDEs), i.e., our result holds without any assu
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Convergence of a vector-valued Allen-Cahn system to Brakke's multiphase mean curvature flow#985239
- Martingale deep neural network for very high-dimensional stochastic optimal controls#1075963
- Maximum principle for quasilinear stochastic PDEs with obstacle#1120619
- Super-approximation and optimal $L^2$ convergence of the stabilized BGN method with piecewise linear parametric finite elements for curve-shortening flow#1049069
- A posteriori certification of PDE approximations with particular application to neural networks#684213
Record · ID 1077214
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive