Cover Image

A deep solver for backward stochastic Volterra integral equations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Andersson, Kristoffer, Gnoatto, Alessandro, Trillos, Camilo Andrés García
Format:	Preprint
Published:	2025
Subjects:	Numerical Analysis Machine Learning Probability Mathematical Finance 65C30, 60H20, 60H35, 68T07 G.1.9; G.3; I.2.6; F.2.1
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations
by: Kapllani, Lorenc, et al.
Published: (2024)

A forward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations
by: Kapllani, Lorenc, et al.
Published: (2024)

A deep solver for BSDEs with jumps
by: Andersson, Kristoffer, et al.
Published: (2022)

Error analysis for the deep Kolmogorov method
by: Cîmpean, Iulian, et al.
Published: (2025)

Functional SDE approximation inspired by a deep operator network architecture
by: Eigel, Martin, et al.
Published: (2024)

Non-convergence to the optimal risk for Adam and stochastic gradient descent optimization in the training of deep neural networks
by: Do, Thang, et al.
Published: (2025)

Deep Operator BSDE: a Numerical Scheme to Approximate Solution Operators
by: Lozano, Pere Díaz, et al.
Published: (2024)

Multi-Layer Deep xVA: Structural Credit Models, Measure Changes and Convergence Analysis
by: Andersson, Kristoffer, et al.
Published: (2025)

A forward scheme with machine learning for forward-backward SDEs with jumps by decoupling jumps
by: Kawai, Reiichiro, et al.
Published: (2024)

Deep Quadratic Hedging
by: Gnoatto, Alessandro, et al.
Published: (2022)

A new architecture of high-order deep neural networks that learn martingales
by: Ninomiya, Syoiti, et al.
Published: (2025)

Strong solution of stochastic differential equations with discontinuous and unbounded coefficients
by: Hu, Yaozhong, et al.
Published: (2023)

Comparing EPGP Surrogates and Finite Elements Under Degree-of-Freedom Parity
by: Amo, Obed, et al.
Published: (2025)

A nonnegativity-preserving finite element method for a class of parabolic SPDEs with multiplicative noise
by: Djurdjevac, Ana, et al.
Published: (2025)

Neural Networks for Tamed Milstein Approximation of SDEs with Additive Symmetric Jump Noise Driven by a Poisson Random Measure
by: Ramirez-Gonzalez, Jose-Hermenegildo, et al.
Published: (2025)

Error analysis for learning fractional stochastic differential equations with applications in neural approximations
by: Dehshiri, Mahdi, et al.
Published: (2026)

Non-convergence to global minimizers in data driven supervised deep learning: Adam and stochastic gradient descent optimization provably fail to converge to global minimizers in the training of deep neural networks with ReLU activation
by: Do, Thang, et al.
Published: (2024)

A-FloPS: Accelerating Diffusion Models via Adaptive Flow Path Sampler
by: Jin, Cheng, et al.
Published: (2025)

Data-driven computation for periodic stochastic differential equations
by: Li, Yao, et al.
Published: (2025)

Small-time central limit theorems for stochastic Volterra integral equations and their Markovian lifts
by: Friesen, Martin, et al.
Published: (2024)

A tensor-train reduced basis solver for parameterized partial differential equations on Cartesian grids
by: Mueller, Nicholas, et al.
Published: (2024)

Regulating stochastic clocks
by: Fei, Zhe, et al.
Published: (2022)

Weak Convergence Analysis for the Finite Element Approximation to Stochastic Allen-Cahn Equation Driven by Multiplicative White Noise
by: Zhang, Minxing, et al.
Published: (2025)

Reduced basis solvers for unfitted methods on parameterized domains
by: Mueller, Nicholas, et al.
Published: (2025)

An Operator Ito Formula for Volterra Gaussian Processes: The Intrinsic Bracket via Causal Derivation-Divergence Factorization
by: Fontes, Ramiro
Published: (2026)

The deep multi-FBSDE method: a robust deep learning method for coupled FBSDEs
by: Andersson, Kristoffer, et al.
Published: (2025)

An Euler scheme for BSDEs via the Wiener chaos decomposition
by: Lozano, Pere Díaz, et al.
Published: (2025)

Challenges in automatic differentiation and numerical integration in physics-informed neural networks modelling
by: Daněk, Josef, et al.
Published: (2024)

Learning Hamiltonian flows from numerical integrators and examples
by: Fang, Rui, et al.
Published: (2025)

Malliavin calculus and densities for chaos-driven stochastic differential equations
by: Loosveldt, Laurent, et al.
Published: (2026)

On the short-time behaviour of up-and-in barrier options using Malliavin calculus
by: Burés, Òscar
Published: (2025)

Time evaluation of portfolio for asymmetrically informed traders
by: D'Auria, Bernardo, et al.
Published: (2024)

Neural Network Localized Orthogonal Decomposition for Numerical Homogenization of Diffusion Operators with Random Coefficients
by: Kröpfl, Fabian, et al.
Published: (2025)

On the existence, uniqueness and stability of solutions of SDEs with state-dependent variable exponent
by: Avci, Mustafa
Published: (2025)

Strong convergence in the infinite horizon of numerical methods for stochastic delay differential equations
by: Wang, Yudong, et al.
Published: (2025)

The Euler Scheme for Fractional Stochastic Delay Differential Equations with Additive Noise
by: Sauri, Orimar
Published: (2024)

Fully discrete approximation of the semilinear stochastic wave equation on the sphere
by: Cohen, David, et al.
Published: (2026)

A stochastic Galerkin method with adaptive time-stepping for the Navier-Stokes equations
by: Sousedík, Bedřich, et al.
Published: (2022)

Uniform in time convergence of numerical schemes for stochastic differential equations via Strong Exponential stability: Euler methods, Split-Step and Tamed Schemes
by: Angeli, Letizia, et al.
Published: (2023)

Unbiased estimators for the Heston model with stochastic interest rates
by: Zheng, Chao, et al.
Published: (2023)