Tootfinder

Suck on this, Bondi & Trump.
Original post: https://bsky.app/profile/did:plc:wvwvlbeizca367klhhyudf2n/post/3lwr36whbak27

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Wavefold:
🎵 Techgnosis
#Wavefold
https://wetware-echo.bandcamp.com/album/techgnosis
https://open.spotify.com/track/6DbLRlaAQR6In1Bmh3Au7O

Wavefield Correlation Imaging in Arbitrary Media with Inherent Aberration Correction
Scott Schoen Jr, Brian Lause, Marko Jakovljevic, Rimon Tadross, Mike Washburn, Anthony E. Samir
https://arxiv.org/abs/2508.13017

Wavefield Correlation Imaging in Arbitrary Media with Inherent Aberration Correction
Ultrasound (US) imaging is an indispensable tool for diagnostic imaging, particularly given its cost, safety, and portability profiles compared to other modalities. However, US is challenged in subjects with morphological heterogeneity (e.g., those with overweight or obesity), largely because conventional imaging algorithms do not account for such variation in the beamforming process. Specific knowledge of the these spatial variations enables supplemental corrections of these algorithms, but wi…

Replaced article(s) found for physics.geo-ph. https://arxiv.org/list/physics.geo-ph/new
[1/1]:
- Physics-informed Neural Networks with Fourier Features for Seismic Wavefield Simulation in Time-D...
Yi Ding, Su Chen, Hiroe Miyake, Xiaojun Li

Propulsion and interaction of wave-propelled interfacial particles
Daniel M. Harris, Jack-William Barotta
https://arxiv.org/abs/2507.00247 https://

Propulsion and interaction of wave-propelled interfacial particles
When a floating body is internally or externally vibrated, its self-generated wavefield can lead to steady propulsion along the interface. In this article, we review several related and recently discovered systems that leverage this propulsion mechanism and interact hydrodynamically with one another via these surface waves. Particles with an onboard oscillatory driver may self-propel by virtue of a fore-aft asymmetric wavefield, a phenomenon with demonstrated relevance to biological and artific…

DiffPINN: Generative diffusion-initialized physics-informed neural networks for accelerating seismic wavefield representation
Shijun Cheng, Tariq Alkhalifah
https://arxiv.org/abs/2506.00471

DiffPINN: Generative diffusion-initialized physics-informed neural networks for accelerating seismic wavefield representation
Physics-informed neural networks (PINNs) offer a powerful framework for seismic wavefield modeling, yet they typically require time-consuming retraining when applied to different velocity models. Moreover, their training can suffer from slow convergence due to the complexity of of the wavefield solution. To address these challenges, we introduce a latent diffusion-based strategy for rapid and effective PINN initialization. First, we train multiple PINNs to represent frequency-domain scattered w…

This https://arxiv.org/abs/2407.18820 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

On Godunov-type finite volume methods for seismic wave propagation
The computational complexity of simulating seismic waves demands continual exploration of more efficient numerical methods. While Finite Volume methods are widely acclaimed for tackling general nonlinear hyperbolic (wave) problems, their application in realistic seismic wave simulation remains uncommon, with rare investigations in the literature. Furthermore, seismic wavefields are influenced by sharp subsurface interfaces frequently encountered in realistic models, which could, in principle, b…

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Client 03:
🎵 Wavefile_dayjob
#Client03
https://client03.bandcamp.com/track/wavefile-dayjob
https://open.spotify.com/track/0gmR9uKfKvpYwEJErCs3gU

An effective physics-informed neural operator framework for predicting wavefields
Xiao Ma, Tariq Alkhalifah
https://arxiv.org/abs/2507.16431 https://

An effective physics-informed neural operator framework for predicting wavefields
Solving the wave equation is fundamental for geophysical applications. However, numerical solutions of the Helmholtz equation face significant computational and memory challenges. Therefore, we introduce a physics-informed convolutional neural operator (PICNO) to solve the Helmholtz equation efficiently. The PICNO takes both the background wavefield corresponding to a homogeneous medium and the velocity model as input function space, generating the scattered wavefield as the output function spa…

Physics-informed conditional diffusion model for generalizable elastic wave-mode separation
Shijun Cheng, Xinru Mu, Tariq Alkhalifah
https://arxiv.org/abs/2506.23007

Physics-informed conditional diffusion model for generalizable elastic wave-mode separation
Traditional elastic wavefield separation methods, while accurate, often demand substantial computational resources, especially for large geological models or 3D scenarios. Purely data-driven neural network approaches can be more efficient, but may fail to generalize and maintain physical consistency due to the absence of explicit physical constraints. Here, we propose a physics-informed conditional diffusion model for elastic wavefield separation that seamlessly integrates domain-specific physi…