22 citations · 67 across the 9 of their papers we have counts for
8 papers · 1 filter
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…
Inverse Modeling of Viscoelasticity Materials using Physics Constrained Learning
Kailai Xu, Alexandre M. Tartakovsky, Jeff Burghardt +1
We propose a novel approach to model viscoelasticity materials using neural networks, which capture rate-dependent and nonlinear constitutive relations. However, inputs and outputs…
Learning Constitutive Relations using Symmetric Positive Definite Neural Networks
Kailai Xu, Daniel Z. Huang, Eric Darve
We present the Cholesky-factored symmetric positive definite neural network (SPD-NN) for modeling constitutive relations in dynamical equations. Instead of directly predicting the…