Feng Bao

According to our database, Feng Bao authored at least 39 papers between 2014 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations.

Phuoc-Toan Huynh, Richard Archibald, Feng Bao

CoRR, May, 2026

Finite Expression Method with TranNet-based Function Learning for High-Dimensional Partial Differential Equations.

Phuoc-Toan Huynh, Feng Bao, Haizhao Yang, Ahmed Zytoon

CoRR, April, 2026

Global Attention with Linear Complexity for Exascale Generative Data Assimilation in Earth System Prediction.

Xiao Wang, Zezhong Zhang, Isaac Lyngaas, Hong-Jun Yoon, Jong-Youl Choi, Siming Liang, Janet Wang, Hristo G. Chipilski, Ashwin M. Aji, Feng Bao, Peter Jan van Leeuwen, Dan Lu, Guannan Zhang

CoRR, April, 2026

A Score Filter Enhanced Data Assimilation Framework for Data-Driven Dynamical Systems.

Jingqiao Tang, Ryan Bausback, Feng Bao, Guannan Zhang, Phuoc-Toan Huynh

CoRR, March, 2026

Error estimates of a training-free diffusion model for high-dimensional sampling.

Pengjun Wang, Zezhong Zhang, Minglei Yang, Feng Bao, Yanzhao Cao, Guannan Zhang

CoRR, January, 2026

A score-based diffusion model approach for adaptive learning of stochastic partial differential equation solutions.

Phuoc-Toan Huynh, Ruth Lopez Fajardo, Guannan Zhang, Lili Ju, Feng Bao

J. Comput. Phys., 2026

2025

The Ensemble Schr{ö}dinger Bridge filter for Nonlinear Data Assimilation.

Feng Bao, Hui Sun

CoRR, December, 2025

A Score-based Diffusion Model Approach for Adaptive Learning of Stochastic Partial Differential Equation Solutions.

Toan Huynh, Ruth Lopez Fajardo, Guannan Zhang, Lili Ju, Feng Bao

CoRR, August, 2025

Stochastic Operator Network: A Stochastic Maximum Principle Based Approach to Operator Learning.

Ryan Bausback, Jingqiao Tang, Lu Lu, Feng Bao, Toan Huynh

CoRR, July, 2025

Federated Learning on Stochastic Neural Networks.

Jingqiao Tang, Ryan Bausback, Feng Bao, Richard Archibald

CoRR, June, 2025

Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions.

Hoang Tran, Zezhong Zhang, Feng Bao, Dan Lu, Guannan Zhang

CoRR, May, 2025

Numerical approximations for partially observed optimal control of stochastic partial differential equations.

Feng Bao, Yanzhao Cao, Hongjiang Qian

CoRR, April, 2025

Ensemble score filter with image inpainting for data assimilation in tracking surface quasi-geostrophic dynamics with partial observations.

Siming Liang, Hoang Tran, Feng Bao, Hristo G. Chipilski, Peter Jan van Leeuwen, Guannan Zhang

CoRR, January, 2025

Joint state-parameter estimation for the reduced fracture model via the united filter.

Phuoc-Toan Huynh, Thi-Thao-Phuong Hoang, Guannan Zhang, Feng Bao

J. Comput. Phys., 2025

2024

Transferable Neural Networks for Partial Differential Equations.

Zezhong Zhang, Feng Bao, Lili Ju, Guannan Zhang

J. Sci. Comput., April, 2024

Numerical Analysis for Convergence of a Sample-Wise Backpropagation Method for Training Stochastic Neural Networks.

Richard Archibald, Feng Bao, Yanzhao Cao, Hui Sun

SIAM J. Numer. Anal., 2024

A score-based filter for nonlinear data assimilation.

Feng Bao, Zezhong Zhang, Guannan Zhang

J. Comput. Phys., 2024

Convergence Analysis for A Stochastic Maximum Principle Based Data Driven Feedback Control Algorithm.

Siming Liang, Hui Sun, Richard Archibald, Feng Bao

CoRR, 2024

A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics.

Junqi Yin, Siming Liang, Siyan Liu, Feng Bao, Hristo G. Chipilski, Dan Lu, Guannan Zhang

Proceedings of the SC24-W: Workshops of the International Conference for High Performance Computing, 2024

A Scalable Training-Free Diffusion Model for Uncertainty Quantification.

Ali Haisam Muhammad Rafid, Junqi Yin, Yuwei Geng, Siming Liang, Feng Bao, Lili Ju, Guannan Zhang

Proceedings of the SC24-W: Workshops of the International Conference for High Performance Computing, 2024

2023

A stochastic maximum principle approach for reinforcement learning with parameterized environment.

Richard Archibald, Feng Bao, Jiongmin Yong

J. Comput. Phys., September, 2023

Splitting scheme for backward doubly stochastic differential equations.

Feng Bao, Yanzhao Cao, He Zhang

Adv. Comput. Math., August, 2023

Improving the Expressive Power of Deep Neural Networks through Integral Activation Transform.

Zezhong Zhang, Feng Bao, Guannan Zhang

CoRR, 2023

Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation.

Yanfang Liu, Minglei Yang, Zezhong Zhang, Feng Bao, Yanzhao Cao, Guannan Zhang

CoRR, 2023

An Ensemble Score Filter for Tracking High-Dimensional Nonlinear Dynamical Systems.

Feng Bao, Zezhong Zhang, Guannan Zhang

CoRR, 2023

TransNet: Transferable Neural Networks for Partial Differential Equations.

Zezhong Zhang, Feng Bao, Lili Ju, Guannan Zhang

CoRR, 2023

Parameter Estimation for the Truncated KdV Model through a Direct Filter Method.

Hui Sun, Nick Moore, Feng Bao

CoRR, 2023

2022

Kernel learning backward SDE filter for data assimilation.

Richard Archibald, Feng Bao

J. Comput. Phys., 2022

Convergence Analysis for Training Stochastic Neural Networks via Stochastic Gradient Descent.

Richard Archibald, Feng Bao, Yanzhao Cao, Hui Sun

CoRR, 2022

A PDE-based Adaptive Kernel Method for Solving Optimal Filtering Problems.

Zezhong Zhang, Richard Archibald, Feng Bao

CoRR, 2022

A Kernel Learning Method for Backward SDE Filter.

Richard Archibald, Feng Bao

CoRR, 2022

2021

Meshfree Approximation for Stochastic Optimal Control Problems.

Hui Sun, Feng Bao

CoRR, 2021

Solving Backward Doubly Stochastic Differential Equations through Splitting Schemes.

Feng Bao, Yanzhao Cao, He Zhang

CoRR, 2021

2020

An Efficient Numerical Algorithm for Solving Data Driven Feedback Control Problems.

Richard Archibald, Feng Bao, Jiongmin Yong, Tao Zhou

J. Sci. Comput., 2020

Uncertainty Quantification in Deep Learning through Stochastic Maximum Principle.

Richard Archibald, Feng Bao, Yanzhao Cao, He Zhang

CoRR, 2020

2019

A direct filter method for parameter estimation.

Richard Archibald, Feng Bao, Xuemin Tu

J. Comput. Phys., 2019

2016

A First Order Scheme for Backward Doubly Stochastic Differential Equations.

Feng Bao, Yanzhao Cao, Amnon J. Meir, Weidong Zhao

SIAM/ASA J. Uncertain. Quantification, 2016

Hierarchical optimization for neutron scattering problems.

Feng Bao, Rick Archibald, Dipanshu Bansal, Olivier Delaire

J. Comput. Phys., 2016

2014

A Hybrid Sparse-Grid Approach for Nonlinear Filtering Problems Based on Adaptive-Domain of the Zakai Equation Approximations.

Feng Bao, Yanzhao Cao, Clayton G. Webster, Guannan Zhang

SIAM/ASA J. Uncertain. Quantification, 2014