📝 Publications

JGR: ML and computation
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Diffusion-based subsurface CO2 multiphysics monitoring and forecasting

Xinquan Huang, Fu Wang, Tariq Alkhalifah

Project code

  • The first unified probabilistic forecasting and inversion framework for CO2 storage, which flexibly incorporates multiple physical processes and data types and enable uncertainty quantification.
JCP
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Efficient physics-informed neural networks using hash encoding

Xinquan Huang, Tariq Alkhalifah

Project code

  • An efficient physics-informed neural network framework using hash encoding, which accelerates the convergence of PINN by at least 10 folds.
IEEE TGRS
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Microseismic source imaging using physics-informed neural networks with hard constraints

Xinquan Huang, Tariq Alkhalifah

Project

  • A direct imaging framework for microseismic source location using physics-informed neural networks with hard constraints, which can achieve high-fidelity source location results without numerical simulation and can apply to both irregular 2D and 3D data. The combination of hard constraints and novel depth causality constraint can further improve the source location accuracy. The first PINN-based realistic application.
IEEE TGRS
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A prior regularized full waveform inversion using generative diffusion models

Fu Wang, Xinquan Huang$\dagger$, Tariq Alkhalifah

Project

  • The first generative diffusion framework combined with full waveform inversion to achieve high-fidelity inversion results, which can further incorporate geological and well prior information by conditioning on geological and well data.
ICML 2023
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NeuralStagger: Accelerating physics constrained neural PDE solver with spatial-temporal decomposition

Xinquan Huang, Wenlei Shi, Qi Meng, Yue Wang, Xiaotian Gao, Jia Zhang, Tie-Yan Liu

Project

  • A novel, simple, and general spatiotemporal decomposition strategy that speeds up the solution of partial differential equations using neural networks.
JGR: Solid Earth
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PINNup: Robust neural network wavefield solutions using frequency upscaling and neuron splitting

Xinquan Huang, Tariq Alkhalifah

Project [code]

  • A novel physics-informed neural network framework using frequency upscaling and neuron splitting, yielding efficient, highly accurate, high-frequency wavefield solutions. The first PINN framework to achieve 32Hz wavefield solutions.

Selected Publications ($\dagger$: Corresponding Author)

Conference Papers