Gabor-Enhanced Physics-Informed Neural Networks for Fast Simulations of Acoustic Wavefields

• URL: http://arxiv.org/abs/2502.17134v2
• Date: Fri, 28 Feb 2025 06:43:21 GMT
• Title: Gabor-Enhanced Physics-Informed Neural Networks for Fast Simulations of Acoustic Wavefields
• Authors: Mohammad Mahdi Abedi, David Pardo, Tariq Alkhalifah,
• Abstract summary: Physics-Informed Neural Networks (PINNs) have gained increasing attention for solving partial differential equations.<n>We propose a simplified PINN framework that incorporates Gabor functions.<n>We demonstrate its superior accuracy, faster convergence, and better robustness features compared to both traditional PINNs and earlier Gabor-based PINNs.
• Score: 2.8948274245812327
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Physics-Informed Neural Networks (PINNs) have gained increasing attention for solving partial differential equations, including the Helmholtz equation, due to their flexibility and mesh-free formulation. However, their low-frequency bias limits their accuracy and convergence speed for high-frequency wavefield simulations. To alleviate these problems, we propose a simplified PINN framework that incorporates Gabor functions, designed to capture the oscillatory and localized nature of wavefields more effectively. Unlike previous attempts that rely on auxiliary networks to learn Gabor parameters, we redefine the network's task to map input coordinates to a custom Gabor coordinate system, simplifying the training process without increasing the number of trainable parameters compared to a simple PINN. We validate the proposed method across multiple velocity models, including the complex Marmousi and Overthrust models, and demonstrate its superior accuracy, faster convergence, and better robustness features compared to both traditional PINNs and earlier Gabor-based PINNs. Additionally, we propose an efficient integration of a Perfectly Matched Layer (PML) to enhance wavefield behavior near the boundaries. These results suggest that our approach offers an efficient and accurate alternative for scattered wavefield modeling and lays the groundwork for future improvements in PINN-based seismic applications.

Related papers

• Multi-Preconditioned LBFGS for Training Finite-Basis PINNs [35.66877569643008]
  The MP-LBFGS algorithm is introduced for training finite-basis physics-informed neural networks (FBPINNs)<n>A key feature is a novel nonlinear multi-preconditioning mechanism, in which subdomain corrections are optimally combined through the solution of a low-dimensional subspace minimization problem.<n> Numerical experiments indicate that MP-LBFGS can improve convergence speed, as well as model accuracy over standard LBFGS while incurring lower communication overhead.
  arXiv  Detail & Related papers  (2026-01-13T16:38:15Z)
• Graph Network-based Structural Simulator: Graph Neural Networks for Structural Dynamics [40.190675168132124]
  We introduce the Graph Network-based Structural Simulator (GNSS), a GNN framework for surrogate modeling of dynamic structural problems.<n>We evaluate on a case study involving a beam excited by a 50kHz Hanning-modulated pulse.<n>The results show that accurately reproduces the physics of the problem over hundreds of timesteps and generalizes to unseen loading conditions, where existing GNNs fail to converge or deliver meaningful predictions.
  arXiv  Detail & Related papers  (2025-10-29T16:47:24Z)
• Unveiling the Spatial-temporal Effective Receptive Fields of Spiking Neural Networks [47.413471945080566]
  Spiking Neural Networks (SNNs) demonstrate significant potential for energy-efficient neuromorphic computing.<n>In artificial neural networks, the effective receptive field (ERF) serves as a valuable tool for analyzing feature extraction capabilities.<n>We introduce the Spatio-Temporal Effective Receptive Field (ST-ERF) to analyze the ERF distributions across various Transformer-based SNNs.
  arXiv  Detail & Related papers  (2025-10-24T12:46:58Z)
• DiffPINN: Generative diffusion-initialized physics-informed neural networks for accelerating seismic wavefield representation [3.069335774032178]
  Physics-informed neural networks (PINNs) offer a powerful framework for seismic wavefield modeling.<n>PINNs typically require time-consuming retraining when applied to different velocity models.<n>We introduce a latent diffusion-based strategy for rapid and effective PINN initialization.
  arXiv  Detail & Related papers  (2025-05-31T08:41:06Z)
• Least-Squares-Embedded Optimization for Accelerated Convergence of PINNs in Acoustic Wavefield Simulations [2.8948274245812327]
  PINNs have shown promise in solving partial differential equations. For scattered acoustic wavefield simulation based on Helmholtz equation, we derive a hybrid optimization framework. This framework accelerates training convergence by embedding a least-squares (LS) solver directly into the GD loss function.
  arXiv  Detail & Related papers  (2025-04-23T09:32:14Z)
• Multi-frequency wavefield solutions for variable velocity models using meta-learning enhanced low-rank physics-informed neural network [3.069335774032178]
  Physics-informed neural networks (PINNs) face significant challenges in modeling multi-frequency wavefields in complex velocity models.<n>We propose Meta-LRPINN, a novel framework that combines low-rank parameterization with meta-learning and frequency embedding.<n> Numerical experiments show that Meta-LRPINN achieves much fast convergence speed and much high accuracy compared to baseline methods.
  arXiv  Detail & Related papers  (2025-02-02T20:12:39Z)
• TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals [58.865901821451295]
  We present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain, we introduce a "TC" stream that uses Continuous Wavelet Transform (CWT) to represent information in a 2D tensor form.
  arXiv  Detail & Related papers  (2024-04-15T06:01:48Z)
• Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation [80.227864832092]
  Graph neural networks (GNNs) have exhibited exceptional efficacy in a diverse array of applications. The sheer size of large-scale graphs presents a significant challenge to real-time inference with GNNs. We propose an online propagation framework and two novel node-adaptive propagation methods.
  arXiv  Detail & Related papers  (2023-10-17T05:03:00Z)
• Physics-informed neural wavefields with Gabor basis functions [4.07926531936425]
  We propose an approach to enhance the efficiency and accuracy of neural network wavefield solutions. Specifically, for the Helmholtz equation, we augment the fully connected neural network model with an Gabor layer constituting the final hidden layer. These/coefficients of the Gabor functions are learned from the previous hidden layers that include nonlinear activation functions.
  arXiv  Detail & Related papers  (2023-10-16T17:30:33Z)
• SupeRBNN: Randomized Binary Neural Network Using Adiabatic Superconductor Josephson Devices [44.440915387556544]
  AQFP devices serve as excellent carriers for binary neural network (BNN) computations. We propose SupeRBNN, an AQFP-based randomized BNN acceleration framework. We show that our design achieves an energy efficiency of approximately 7.8x104 times higher than that of the ReRAM-based BNN framework.
  arXiv  Detail & Related papers  (2023-09-21T16:14:42Z)
• GaborPINN: Efficient physics informed neural networks using multiplicative filtered networks [0.0]
  Physics-informed neural networks (PINNs) provide functional wavefield solutions represented by neural networks (NNs) We propose a modified PINN using multiplicative filtered networks, which embeds some of the known characteristics of the wavefield in training. The proposed method achieves up to a two-magnitude increase in the speed of convergence as compared with conventional PINNs.
  arXiv  Detail & Related papers  (2023-08-10T19:51:00Z)
• Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data [37.69303106863453]
  We propose a novel approach for phase-resolved wave surface reconstruction using neural networks. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids.
  arXiv  Detail & Related papers  (2023-05-18T12:30:26Z)
• Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks [51.92362217307946]
  Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems. PINNs are trapped in training failures when the target functions to be approximated exhibit high-frequency or multi-scale features. In this paper, we propose to employ implicit gradient descent (ISGD) method to train PINNs for improving the stability of training process.
  arXiv  Detail & Related papers  (2023-03-03T08:17:47Z)
• Neural Basis Functions for Accelerating Solutions to High Mach Euler Equations [63.8376359764052]
  We propose an approach to solving partial differential equations (PDEs) using a set of neural networks. We regress a set of neural networks onto a reduced order Proper Orthogonal Decomposition (POD) basis. These networks are then used in combination with a branch network that ingests the parameters of the prescribed PDE to compute a reduced order approximation to the PDE.
  arXiv  Detail & Related papers  (2022-08-02T18:27:13Z)
• Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design [68.86220939532373]
  The finite precision level is captured through the use of quantized neural networks (QNNs) that quantize weights and activations in fixed-precision format. The proposed FL framework can reduce energy consumption until convergence by up to 70% compared to a baseline FL algorithm.
  arXiv  Detail & Related papers  (2022-07-19T16:37:24Z)
• PINNup: Robust neural network wavefield solutions using frequency upscaling and neuron splitting [0.0]
  We propose a novel implementation of PINN using frequency upscaling and neuron splitting. The proposed PINN exhibits notable superiority in terms of convergence and accuracy. It can achieve neuron based high-frequency wavefield solutions with a two-hidden-layer model.
  arXiv  Detail & Related papers  (2021-09-29T16:35:50Z)
• U-FNO -- an enhanced Fourier neural operator based-deep learning model for multiphase flow [43.572675744374415]
  We present U-FNO, an enhanced Fourier neural operator for solving the multiphase flow problem. We show that the U-FNO architecture has the advantages of both traditional CNN and original FNO, providing significantly more accurate and efficient performance. The trained U-FNO provides gas saturation and pressure buildup predictions with a 10,000 times speedup compared to traditional numerical simulators.
  arXiv  Detail & Related papers  (2021-09-03T17:52:25Z)
• Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation [10.775558382613077]
  Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. We propose a deep learning-based approach that directly optimize the beamformers at the base station according to the received uplink pilots. A neural calibration method is proposed to improve the scalability of the end-to-end design.
  arXiv  Detail & Related papers  (2021-08-03T14:26:14Z)
• Bayesian Graph Neural Networks with Adaptive Connection Sampling [62.51689735630133]
  We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) The proposed framework not only alleviates over-smoothing and over-fitting tendencies of deep GNNs, but also enables learning with uncertainty in graph analytic tasks with GNNs.
  arXiv  Detail & Related papers  (2020-06-07T07:06:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.