83 citations · 290 across the 16 of their papers we have counts for
16 papers
Self-Flow-Matching assisted Full Waveform Inversion
Xinquan Huang, Paris Perdikaris
Full-waveform inversion (FWI) is a high-resolution seismic imaging method that estimates subsurface velocity by matching simulated and recorded waveforms. However, FWI is highly no…
CFO: Learning Continuous-Time PDE Dynamics via Flow-Matched Neural Operators
Xianglong Hou, Xinquan Huang, Paris Perdikaris
Neural operator surrogates for time-dependent partial differential equations (PDEs) conventionally employ autoregressive prediction schemes, which accumulate error over long rollou…
Physics-informed waveform inversion using pretrained wavefield neural operators
Xinquan Huang, Fu Wang, Tariq Alkhalifah
Full waveform inversion (FWI) is crucial for reconstructing high-resolution subsurface models, but it is often hindered, considering the limited data, by its null space resulting i…
PhysicsCorrect: A Training-Free Approach for Stable Neural PDE Simulations
Xinquan Huang, Paris Perdikaris
Neural networks have emerged as powerful surrogates for solving partial differential equations (PDEs), offering significant computational speedups over traditional methods. However…
Geological and Well prior assisted full waveform inversion using conditional diffusion models
Fu Wang, Xinquan Huang, Tariq Alkhalifah
Full waveform inversion (FWI) often faces challenges due to inadequate seismic observations, resulting in band-limited and geologically inaccurate inversion results. Incorporating…
Diffusion-based subsurface CO multiphysics monitoring and forecasting
Xinquan Huang, Fu Wang, Tariq Alkhalifah
Carbon capture and storage (CCS) plays a crucial role in mitigating greenhouse gas emissions, particularly from industrial outputs. Using seismic monitoring can aid in an accurate…
Learned frequency-domain scattered wavefield solutions using neural operators
Xinquan Huang, Tariq Alkhalifah
Solving the wave equation is essential to seismic imaging and inversion. The numerical solution of the Helmholtz equation, fundamental to this process, often encounters significant…
Controllable seismic velocity synthesis using generative diffusion models
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. Machine learning-based…
Physics-informed neural wavefields with Gabor basis functions
Tariq Alkhalifah, Xinquan Huang
Recently, Physics-Informed Neural Networks (PINNs) have gained significant attention for their versatile interpolation capabilities in solving partial differential equations (PDEs)…
GaborPINN: Efficient physics informed neural networks using multiplicative filtered networks
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. Physics-informed neural network…
A prior regularized full waveform inversion using generative diffusion models
Fu Wang, Xinquan Huang, Tariq Alkhalifah
Full waveform inversion (FWI) has the potential to provide high-resolution subsurface model estimations. However, due to limitations in observation, e.g., regional noise, limited s…
Microseismic source imaging using physics-informed neural networks with hard constraints
Xinquan Huang, Tariq Alkhalifah
Microseismic source imaging plays a significant role in passive seismic monitoring. However, such a process is prone to failure due to aliasing when dealing with sparsely measured…
Efficient physics-informed neural networks using hash encoding
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) solutio…
NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition
Xinquan Huang, Wenlei Shi, Qi Meng +4
Neural networks have shown great potential in accelerating the solution of partial differential equations (PDEs). Recently, there has been a growing interest in introducing physics…
LordNet: An Efficient Neural Network for Learning to Solve Parametric Partial Differential Equations without Simulated Data
Xinquan Huang, Wenlei Shi, Xiaotian Gao +5
Neural operators, as a powerful approximation to the non-linear operators between infinite-dimensional function spaces, have proved to be promising in accelerating the solution of…
PINNup: Robust neural network wavefield solutions using frequency upscaling and neuron splitting
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. However, when dealing with hi…