Physics‐Informed Machine Learning Method for Large‐Scale Data Assimilation Problems. [No authors listed] YH Yeung, DA Barajas‐Solano… - Water Resources …, 2022 - Wiley Online Library GSID: Od5WWYmE-FgJ
Improving simulation efficiency of MCMC for inverse modeling of hydrologic systems with a Kalman‐inspired proposal distribution. Lin G, Shi X, Vrugt JA, Wu L, Zhang J. J Zhang, JA Vrugt, X Shi, G Lin, L Wu… - Water Resources …, 2020 - Wiley Online Library GSID: 4jz7h0AwPF8J
Hydrogeophysical characterization of nonstationary DNAPL source zones by integrating a convolutional variational autoencoder and ensemble smoother. Kang X, Kitanidis PK, Kokkinaki A, Shi X. X Kang, A Kokkinaki, PK Kitanidis, X Shi… - Water Resources …, 2021 - Wiley Online Library GSID: ZaQzFlee82EJ
Integrating scientific knowledge with machine learning for engineering and environmental systems. Jia X, Steinbach M, Willard J, Xu S. J Willard, X Jia, S Xu, M Steinbach… - ACM Computing Surveys …, 2021 - dl.acm.org GSID: mX9xlnWftPkJ
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks. Choi Y, Fuhg JN, Kadeethum T, O'Malley D. T Kadeethum, D O'Malley, JN Fuhg, Y Choi… - Nature Computational …, 2021 - nature.com GSID: NwuyyShVefYJ
