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 |
Rhino 3D to Abaqus: A T-spline based isogeometric analysis software framework Y Lai, L Liu, YJ Zhang, J Chen, E Fang, J Lua Advances in Computational Fluid-Structure Interaction and Flow Simulation …, 2016 | 17 | 2016 |
Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport R Baptista, L Cao, J Chen, O Ghattas, F Li, YM Marzouk, JT Oden Journal of Computational Physics 503, 112844, 2024 | 13 | 2024 |
A stochastic stein variational newton method A Leviyev, J Chen, Y Wang, O Ghattas, A Zimmerman arXiv preprint arXiv:2204.09039, 2022 | 12 | 2022 |
LazyDINO: Fast, scalable, and efficiently amortized Bayesian inversion via structure-exploiting and surrogate-driven measure transport L Cao, J Chen, M Brennan, T O'Leary-Roseberry, Y Marzouk, O Ghattas arXiv preprint arXiv:2411.12726, 2024 | | 2024 |
Gaussian mixture Taylor approximations of risk measures constrained by PDEs with Gaussian random field inputs D Luo, J Chen, P Chen, O Ghattas arXiv preprint arXiv:2408.06615, 2024 | | 2024 |