Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration

1 code implementation • 8 Mar 2023 • Zhongyi Jiang, Min Zhu, Dongzhuo Li, Qiuzi Li, Yanhua O. Yuan, Lu Lu

Here, we develop a Fourier-enhanced multiple-input neural operator (Fourier-MIONet) to learn the solution operator of the problem of multiphase flow in porous media.

Differentiable Gaussianization Layers for Inverse Problems Regularized by Deep Generative Models

1 code implementation • 7 Dec 2021 • Dongzhuo Li

However, the latent tensors of such deep generative models can fall out of the desired high-dimensional standard Gaussian distribution during inversion, particularly in the presence of data noise and inaccurate forward models, leading to low-fidelity solutions.

Compressive Sensing Deblurring +1

Time-lapse Full Waveform Inversion for Subsurface Flow Problems with Intelligent Automatic Differentiation

1 code implementation • 16 Dec 2019 • Dongzhuo Li, Kailai Xu, Jerry M. Harris, Eric Darve

We describe a novel framework for PDE (partial-differential-equation)-constrained full-waveform inversion (FWI) that estimates parameters of subsurface flow processes, such as rock permeability and porosity, using time-lapse observed data.

Geophysics

Learning Hidden Dynamics using Intelligent Automatic Differentiation

1 code implementation • 16 Dec 2019 • Kailai Xu, Dongzhuo Li, Eric Darve, Jerry M. Harris

Numerical tests demonstrate the feasibility of IAD for learning hidden dynamics in complicated systems of PDEs; additionally, by incorporating custom built state adjoint method codes in IAD, we significantly accelerate the forward and inverse simulation.

Numerical Analysis Numerical Analysis