Energy security and the reduction of greenhouse gases such as carbon dioxide are two
major crises facing the world today. Using carbon dioxide to develop unconventional oil and …

Learning operators with deep neural networks is an emerging paradigm for scientific
computing. Deep Operator Network (DeepONet) is a modular operator learning framework …

Fourier neural operators (FNOs) are a recently introduced neural network architecture for
learning solution operators of partial differential equations (PDEs), which have been shown …

Solving multiphysics-based inverse problems for geological carbon storage monitoring can
be challenging when multimodal time-lapse data are expensive to collect and costly to …

Despite their remarkable success in approximating a wide range of operators defined by
PDEs, existing neural operators (NOs) do not necessarily perform well for all physics …

In this paper, we firstly extend the Fourier neural operator (FNO) to discovery the mapping
between two infinite-dimensional function spaces, where one is the fractional-order index …

Carbon capture and storage (CCS) is an essential technology for achieving carbon
neutrality. Depositional environments with sandstone and interbedded shale layers are …

Abstract We present the Seismic Laboratory for Imaging and Modeling/Monitoring open-
source software framework for computational geophysics and, more generally, inverse …

Carbon capture and storage (CCS) is an important strategy for reducing carbon dioxide
emissions and mitigating climate change. We consider the storage aspect of CCS, which …

Time-lapse seismic monitoring necessitates integrated workflows that combine seismic and
reservoir modeling to enhance reservoir property estimation. We present a feasibility study …