no code implementations • 25 Jul 2024 • Xinquan Huang, Fu Wang, Tariq Alkhalifah

Meanwhile, due to the generative nature of the approach, we can quantify uncertainty in the prediction.

no code implementations • 9 Feb 2024 • Fu Wang, Xinquan Huang, Tariq Alkhalifah

Accurate seismic velocity estimations are vital to understanding Earth's subsurface structures, assessing natural resources, and evaluating seismic hazards.

no code implementations • 16 Oct 2023 • Tariq Alkhalifah, Xinquan Huang

Specifically, for the Helmholtz equation, we augment the fully connected neural network model with an adaptable Gabor layer constituting the final hidden layer, employing a weighted summation of these Gabor neurons to compute the predictions (output).

no code implementations • 10 Aug 2023 • Xinquan Huang, Tariq Alkhalifah

The computation of the seismic wavefield by solving the Helmholtz equation is crucial to many practical applications, e. g., full waveform inversion.

no code implementations • 22 Jun 2023 • Fu Wang, Xinquan Huang, Tariq Alkhalifah

Specifically, we pre-train a diffusion model in a fully unsupervised manner on a prior velocity model distribution that represents our expectations of the subsurface and then adapt it to the seismic observations by incorporating the FWI into the sampling process of the generative diffusion models.

no code implementations • 9 Apr 2023 • Xinquan Huang, Tariq Alkhalifah

To be more specific, we modify the representation of the frequency-domain wavefield to inherently satisfy the boundary conditions (the measured data on the surface) by means of a hard constraint, which helps to avoid the difficulty in balancing the data and PDE losses in PINNs.

no code implementations • 26 Feb 2023 • Xinquan Huang, Tariq Alkhalifah

Physics-informed neural networks (PINNs) have attracted a lot of attention in scientific computing as their functional representation of partial differential equation (PDE) solutions offers flexibility and accuracy features.

no code implementations • 20 Feb 2023 • Xinquan Huang, Wenlei Shi, Qi Meng, Yue Wang, Xiaotian Gao, Jia Zhang, Tie-Yan Liu

Neural networks have shown great potential in accelerating the solution of partial differential equations (PDEs).

no code implementations • 19 Jun 2022 • Xinquan Huang, Wenlei Shi, Xiaotian Gao, Xinran Wei, Jia Zhang, Jiang Bian, Mao Yang, Tie-Yan Liu

We investigate the physical information in the MSR loss, which we called long-range entanglements, and identify the challenge that the neural network requires the capacity to model the long-range entanglements in the spatial domain of the PDE, whose patterns vary in different PDEs.

no code implementations • 29 Sep 2021 • Xinquan Huang, Tariq Alkhalifah

Solving for the frequency-domain scattered wavefield via physics-informed neural network (PINN) has great potential in seismic modeling and inversion.

no code implementations • NeurIPS Workshop AI4Scien 2021 • Xinquan Huang, Tariq Alkhalifah

However, the neural network (NN) training can be costly and the cost dramatically increases as we train for multi-frequency wavefields by adding frequency to the NN multidimensional function, as the variation of the wavefield with frequency adds more complexity to the NN training.

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