arXiv Machine Learning
Autoencoder-Based Parameter Estimation for Superposed Multi-Component Damped Sinusoidal Signals
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arXiv Machine Learning
Jun 30
Spectral Gating via Damped Oscillations for Adaptive Implicit Neural Representations
arXiv:2606. 23129v2 Announce Type: replace-cross Abstract: Implicit Neural Representations (INRs) have been proven successful in encoding continuous signals through coordinate-based networks, yet facing a spectral dilemma: periodic activations capture fine details but act as all-pass filters that memorise noise, while spatially compact activations regularise effectively but suffer from low-frequency bias.
arXiv Machine Learning
Aug 27
ROMNet: a hybrid reduced order modeling and machine learning approach to waveform inversion
ROMNet is a hybrid reduced‑order modeling and machine‑learning framework designed to improve waveform inversion for acoustic waves. It replaces the costly nonlinear mapping from a reduced‑order model (ROM) matrix to wave speed with a neural network that outputs a simpler ROM matrix, thereby reducing computational effort. The method is validated on two training datasets—random Gaussian‑based media and the GeoFWI benchmark—and compared against direct ROM inversion and two deep‑learning FWI approaches, Fourier‑DeepONet and InversionNet.
arXiv Machine Learning
Jul 7
Uncertainty-aware damage identification in short-span bridges via physics-informed variational autoencoder
arXiv:2607. 05025v1 Announce Type: new Abstract: Vibration-based damage identification in civil infrastructure is a challenging, ill-posed inverse problem due to measurement noise, sparse sensor arrays, and environmental variability.
arXiv Machine Learning
Sep 2
Inverse Reconstruction of Shock Time Series from Shock Response Spectrum Curves using Machine Learning
The paper introduces a conditional variational autoencoder (CVAE) that learns to map shock response spectrum (SRS) curves back to acceleration time series, addressing the ill‑posed inverse problem. Unlike traditional iterative optimization methods that rely on predefined sinusoidal bases, the CVAE provides a data‑driven, non‑iterative solution. Experiments show the model achieves higher spectral fidelity, generalizes well to unseen spectra, and runs three to six orders of magnitude faster than classical techniques.
arXiv Machine Learning
Jul 7
Physics-Informed Domain-Invariant Feature Learning with Autoencoder-Driven Gaussian Clustering for Robust Non-line-of-Sight Scenarios
arXiv:2607. 02537v1 Announce Type: cross Abstract: Jamming and spoofing pose significant threats to wireless and satellite navigation by disrupting radio-frequency (RF) signals and compromising availability and integrity.
arXiv Machine Learning
Aug 7
Kastor: An efficient fine-tuning strategy for generative emulation of PDE simulations
arXiv:2608. 06107v1 Announce Type: new Abstract: Machine learning offers a promising avenue to accelerate physical simulations by replacing computationally expensive traditional Partial Differential Equation (PDE) solvers with fast, differentiable surrogate models.