A Bayesian inference approach to identify a Robin coefficient in one-dimensional parabolic problems. - Liang Yan 002; Fenglian Yang; Chu-Li Fu (J. Comput. Appl. Math., 2009)

A meshless method for solving an inverse spacewise-dependent heat source problem. - Liang Yan 002; Fenglian Yang; Chu-Li Fu (J. Comput. Phys., 2009)

Reconstruction of the corrosion boundary for the Laplace equation by using a boundary collocation method. - Fenglian Yang; Liang Yan 002; Ting Wei (Math. Comput. Simul., 2009)

Approximate inverse method for stable analytic continuation in a strip domain. - Yuan-Xiang Zhang; Chu-Li Fu; Liang Yan 002 (J. Comput. Appl. Math., 2011)

On the interface identification of free boundary problem by method of fundamental solution. - Haifeng Zhao; Liang Yan 002; Jijun Liu (Numer. Linear Algebra Appl., 2013)

Efficient Kansa-type MFS algorithm for time-fractional inverse diffusion problems. - Liang Yan 002; Fenglian Yang (Comput. Math. Appl., 2014)

The method of approximate particular solutions for the time-fractional diffusion equation with a non-local boundary condition. - Liang Yan 002; Fenglian Yang (Comput. Math. Appl., 2015)

Stochastic Collocation Algorithms Using l1-Minimization for Bayesian Solution of Inverse Problems. - Liang Yan 002; Ling Guo (SIAM J. Sci. Comput., 2015)

Sparse Approximation using ℓ1-ℓ2 Minimization and Its Application to Stochastic Collocation. - Liang Yan 002; Yeonjong Shin; Dongbin Xiu (SIAM J. Sci. Comput., 2017)

Doubly stochastic radial basis function methods. - Fenglian Yang; Liang Yan 002; Leevan Ling (J. Comput. Phys., 2018)

Weighted Approximate Fekete Points: Sampling for Least-Squares Polynomial Approximation. - Ling Guo; Akil Narayan 001; Liang Yan 002; Tao Zhou 010 (SIAM J. Sci. Comput., 2018)

An adaptive surrogate modeling based on deep neural networks for large-scale Bayesian inverse problems. - Liang Yan 002; Tao Zhou 010 (CoRR, 2019)

Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems. - Liang Yan 002; Tao Zhou 010 (J. Comput. Phys., 2019)

Gradient-free Stein variational gradient descent with kernel approximation. - Liang Yan 002; Xiling Zou (Appl. Math. Lett., 2021)

Stein variational gradient descent with local approximations. - Liang Yan 002; Tao Zhou 010 (CoRR, 2021)

An acceleration strategy for randomize-then-optimize sampling via deep neural networks. - Liang Yan 002; Tao Zhou 010 (CoRR, 2021)

Optimal design for kernel interpolation: Applications to uncertainty quantification. - Akil Narayan 001; Liang Yan 002; Tao Zhou 010 (J. Comput. Phys., 2021)

Failure-informed adaptive sampling for PINNs, Part II: combining with re-sampling and subset simulation. - Zhiwei Gao; Tao Tang; Liang Yan 002; Tao Zhou 010 (CoRR, 2023)

Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks. - Yongchao Li; Yanyan Wang; Liang Yan 002 (J. Comput. Phys., 2023)

Failure-Informed Adaptive Sampling for PINNs. - Zhiwei Gao; Liang Yan 002; Tao Zhou 010 (SIAM J. Sci. Comput., 2023)

Adaptive Operator Learning for Infinite-Dimensional Bayesian Inverse Problems. - Zhiwei Gao; Liang Yan 002; Tao Zhou 010 (SIAM/ASA J. Uncertain. Quantification, 2024)

Inverse elastic scattering by random periodic structures. - Hao Gu; Xiang Xu; Liang Yan 002 (J. Comput. Phys., 2024)

Deep learning-enhanced reduced-order ensemble Kalman filter for efficient Bayesian data assimilation of parametric PDEs. - Yanyan Wang; Liang Yan 002; Tao Zhou 010 (Comput. Phys. Commun., 2025)

TDDM: A transfer learning framework for physics-guided 3D acoustic scattering inversion. - Yunwen Yin; Liang Yan 002 (J. Comput. Phys., 2025)

Physics-Aware Deep Learning Framework for the Limited Aperture Inverse Obstacle Scattering Problem. - Yunwen Yin; Liang Yan 002 (SIAM J. Sci. Comput., 2025)