{"total":18,"rows":[{"id":"2303.02219","title":"NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training","created":"2023-03-03","cited_by_count":13,"area":null},{"id":"2201.06854","title":"Convergence of a robust deep FBSDE method for stochastic control","created":"2022-01-18","cited_by_count":12,"area":null},{"id":"2405.05192","title":"Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs","created":"2024-05-08","cited_by_count":12,"area":null},{"id":"2212.11518","title":"Mean-field neural networks-based algorithms for McKean-Vlasov control problems *","created":"2022-12-22","cited_by_count":10,"area":null},{"id":"2307.15496","title":"From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs","created":"2023-07-28","cited_by_count":5,"area":null},{"id":"2404.08456","title":"A backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations","created":"2024-04-12","cited_by_count":4,"area":null},{"id":"2412.11010","title":"FBSJNN: A Theoretically Interpretable and Efficiently Deep Learning method for Solving Partial Integro-Differential Equations","created":"2024-12-15","cited_by_count":4,"area":null},{"id":"2511.01125","title":"One model to solve them all: 2BSDE families via neural operators","created":"2025-11-03","cited_by_count":4,"area":null},{"id":"2409.20431","title":"Multilevel Picard approximations and deep neural networks with ReLU, leaky ReLU, and softplus activation overcome the curse of dimensionality when approximating semilinear parabolic partial differential equations in $L^p$-sense","created":"2024-09-30","cited_by_count":3,"area":null},{"id":"2311.07231","title":"Error Analysis of Option Pricing via Deep PDE Solvers: Empirical Study","created":"2023-11-13","cited_by_count":2,"area":null},{"id":"2310.03393","title":"Uncertainty quantification for deep learning-based schemes for solving high-dimensional backward stochastic differential equations","created":"2023-10-05","cited_by_count":1,"area":null},{"id":"2505.18297","title":"A deep solver for backward stochastic Volterra integral equations","created":"2025-05-23","cited_by_count":1,"area":null},{"id":"2408.05620","title":"A forward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations","created":"2024-08-10","cited_by_count":0,"area":null},{"id":"2503.02983","title":"BLADE: Bayesian Langevin Active Discovery with Replica Exchange for Identification of Complex Systems","created":"2025-03-04","cited_by_count":0,"area":null},{"id":"2508.17167","title":"Error analysis for the deep Kolmogorov method","created":"2025-08-23","cited_by_count":0,"area":null},{"id":"2511.07261","title":"High-dimensional Bayesian filtering through deep density approximation","created":"2025-11-10","cited_by_count":0,"area":null},{"id":"2605.14643","title":"Unbiased and Second-Order-Free Training for High-Dimensional PDEs","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2609.05434","title":"Deep Learning for Reflected BSDEs: Regularization and Error Analysis","created":"2026-06-27","cited_by_count":0,"area":null}],"next_cursor":null}