Derives computable a posteriori error bounds for decoupled neural approximations of fully coupled FBSDEs that depend on terminal defect, pathwise residual, and control mismatch, backed by continuous-time stability estimates and numerical tests.
Classical solution to a multidimensional stochastic Burgers equation via forward-backward SDEs
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
In this paper, we address the problem of existence and uniqueness of a global classical solution to a multidimensional stochastic Burgers equation without gradient-type assumptions on the force or the initial condition. The equation is first transformed to a random PDE, and then solved via the associated forward-backward SDE. Additionally, we obtain a new a priori gradient estimate valid for a large class of second-order quasilinear parabolic PDEs which becomes an important tool in our approach. Also, we study the stochastic Burgers equation in the vanishing viscosity limit.
fields
math.OC 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Posteriori Error Analysis for Decoupled Neural Approximations of Fully Coupled FBSDEs with Control Mismatch
Derives computable a posteriori error bounds for decoupled neural approximations of fully coupled FBSDEs that depend on terminal defect, pathwise residual, and control mismatch, backed by continuous-time stability estimates and numerical tests.