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12 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Latest papers 62

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Inverse Reconstruction of Shock Time Series from Shock Response Spectrum Curves using Machine Learning
Mar 3, 2026Adam Watts, Andrew Jeon, Destry Newton +1Signal Reconstruction Shock Propagation
Los Alamos National Laboratory · University of Rochester
Pixelwise Uncertainty Quantification of Accelerated MRI Reconstruction
Jan 19, 2026Ilias I. Giannakopoulos, Lokesh B Gautham Muthukumar, Yvonne W. Lui +1Magnetic Resonance Imaging Reconstruction Magnetic Resonance Imaging
Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, 10016, New York, NY, United States of America · Courant Institute of Mathematical Sciences, NYU, 10012, New York, NY, United States of America · Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Grossman School of Medicine, 10016, New York, NY, United States of America

Mar 3, 2026, cs.LGJ/K move · Enter open · S save

Inverse Reconstruction of Shock Time Series from Shock Response Spectrum Curves using Machine Learning

Adam Watts, Andrew Jeon, Destry Newton, Ryan Bowering

Los Alamos National Laboratory · University of Rochester

Signal Reconstruction Shock Propagation Conditional Variational Autoencoder Acceleration 6-Dof

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The shock response spectrum (SRS) is widely used to characterize the response of single-degree-of-freedom (SDOF) systems to transient accelerations. Because the mapping from acceleration time history to SRS is nonlinear and many-to-one, reconstructing time-domain signals from a target spectrum is inherently ill-posed. Conventional approaches address this problem through iterative optimization, typically representing signals as sums of exponentially decayed sinusoids, but these methods are computationally expensive and constrained by predefined basis functions. We propose a conditional variational autoencoder (CVAE) that learns a data-driven inverse mapping from SRS to acceleration time series. Once trained, the model generates signals consistent with prescribed target spectra without requiring iterative optimization. Experiments demonstrate improved spectral fidelity relative to classical techniques, strong generalization to unseen spectra, and inference speeds three to six orders of magnitude faster. These results establish deep generative modeling as a scalable and efficient approach for inverse SRS reconstruction.

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Latest papers 62

Inverse Reconstruction of Shock Time Series from Shock Response Spectrum Curves using Machine Learning

Pixelwise Uncertainty Quantification of Accelerated MRI Reconstruction