Meta-LRPINN combines SVD low-rank weights, a frequency-embedding hypernetwork, and meta-learned initialization to model multi-frequency seismic wavefields faster and more accurately than baseline PINNs on tested models.
Frequency-domain elastic wave modeling by finite differences: A tool for crosshole seismic imaging
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Multi-frequency wavefield solutions for variable velocity models using meta-learning enhanced low-rank physics-informed neural network
Meta-LRPINN combines SVD low-rank weights, a frequency-embedding hypernetwork, and meta-learned initialization to model multi-frequency seismic wavefields faster and more accurately than baseline PINNs on tested models.