Projected Stein variational Newton: A fast and scalable Bayesian inference method in high dimensions P Chen, K Wu, J Chen, T O'Leary-Roseberry, O Ghattas Advances in Neural Information Processing Systems 32, 2019 | 71 | 2019 |
A fast and scalable computational framework for large-scale high-dimensional Bayesian optimal experimental design K Wu, P Chen, O Ghattas SIAM/ASA Journal on Uncertainty Quantification 11 (1), 235-261, 2023 | 47 | 2023 |
Bayesian inference of heterogeneous epidemic models: Application to COVID-19 spread accounting for long-term care facilities P Chen, K Wu, O Ghattas Computer Methods in Applied Mechanics and Engineering 385, 114020, 2021 | 26 | 2021 |
Large-scale Bayesian optimal experimental design with derivative-informed projected neural network K Wu, T O’Leary-Roseberry, P Chen, O Ghattas Journal of Scientific Computing 95 (1), 30, 2023 | 23 | 2023 |
A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification K Wu, J Li Journal of Computational Physics 321, 1098-1109, 2016 | 15 | 2016 |
An offline-online decomposition method for efficient linear Bayesian goal-oriented optimal experimental design: Application to optimal sensor placement K Wu, P Chen, O Ghattas SIAM Journal on Scientific Computing 45 (1), B57-B77, 2023 | 11 | 2023 |
An efficient method for goal-oriented linear bayesian optimal experimental design: Application to optimal sensor placemen K Wu, P Chen, O Ghattas arXiv preprint arXiv:2102.06627, 2021 | 11 | 2021 |
Derivative-informed projected neural network for large-scale Bayesian optimal experimental design K Wu, T O’Leary-Roseberry, P Chen, O Ghattas arXiv preprint arXiv:2201.07925, 342, 2022 | 5 | 2022 |
A fast and scalable computational framework for goal-oriented linear bayesian optimal experimental design: application to optimal sensor placement K Wu, P Chen, O Ghattas arXiv preprint arXiv:2102.066271, 2021 | 4 | 2021 |
Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data L Cao, K Wu, J Tinsley Oden, P Chen, O Ghattas arXiv e-prints, arXiv: 2306.05398, 2023 | 2 | 2023 |
Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data and machine learning surrogate L Cao, K Wu, JT Oden, P Chen, O Ghattas Computer Methods in Applied Mechanics and Engineering 417, 116349, 2023 | 1 | 2023 |
Optimal experimental design for large-scale Bayesian inverse problems K Wu | | 2022 |
Transport-Based Variational Bayesian Methods for Learning from Data D Bigoni, J Chen, P Chen, O Ghattas, Y Marzouk, T O’Leary–Roseberry, ... dimensions 3, 4, 0 | | |