22 citations · 67 across the 9 of their papers we have counts for
16 papers
Autonomous Inversion of In Situ Deformation Measurement Data for CO2 Storage Decision Support
Jeff Burghardt, Ting Bao, Kailai Xu +2
Current methods of estimating the change in stress caused by injecting fluid into subsurface formations require choosing the type of constitutive model and the model parameters bas…
Trust Region Method for Coupled Systems of PDE Solvers and Deep Neural Networks
Kailai Xu, Eric Darve
Physics-informed machine learning and inverse modeling require the solution of ill-conditioned non-convex optimization problems. First-order methods, such as SGD and ADAM, and quas…
Integrating Deep Neural Networks with Full-waveform Inversion: Reparametrization, Regularization, and Uncertainty Quantification
Weiqiang Zhu, Kailai Xu, Eric Darve +2
Full-waveform inversion (FWI) is an accurate imaging approach for modeling velocity structure by minimizing the misfit between recorded and predicted seismic waveforms. However, th…
ADCME: Learning Spatially-varying Physical Fields using Deep Neural Networks
Kailai Xu, Eric Darve
ADCME is a novel computational framework to solve inverse problems involving physical simulations and deep neural networks (DNNs). This paper benchmarks its capability to learn spa…
Distributed Machine Learning for Computational Engineering using MPI
Kailai Xu, Weiqiang Zhu, Eric Darve
We propose a framework for training neural networks that are coupled with partial differential equations (PDEs) in a parallel computing environment. Unlike most distributed computi…
Solving Inverse Problems in Steady-State Navier-Stokes Equations using Deep Neural Networks
Tiffany Fan, Kailai Xu, Jay Pathak +1
Inverse problems in fluid dynamics are ubiquitous in science and engineering, with applications ranging from electronic cooling system design to ocean modeling. We propose a genera…