Cover Image

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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Watts, Adam, Jeon, Andrew, Newton, Destry, Bowering, Ryan
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Signal Processing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

mtslearn: Machine Learning in Python for Medical Time Series
by: Jiang, Zhongheng, et al.
Published: (2026)

Consistent Signal Reconstruction from Streaming Multivariate Time Series
by: Ruiz-Moreno, Emilio, et al.
Published: (2023)

RINS-T: Robust Implicit Neural Solvers for Time Series Linear Inverse Problems
by: Niresi, Keivan Faghih, et al.
Published: (2025)

Non-negative DAG Learning from Time-Series Data
by: Rey, Samuel, et al.
Published: (2025)

Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization
by: Karkaria, Vispi, et al.
Published: (2024)

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization
by: Krishnamurthy, Vikram
Published: (2025)

Phase-driven Domain Generalizable Learning for Nonstationary Time Series
by: Mohapatra, Payal, et al.
Published: (2024)

On Sparse High-Dimensional Graphical Model Learning For Dependent Time Series
by: Tugnait, Jitendra K.
Published: (2021)

Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training
by: Burchert, Johannes, et al.
Published: (2024)

Anomaly Detection for Non-stationary Time Series using Recurrent Wavelet Probabilistic Neural Network
by: Yang, Pu, et al.
Published: (2025)

On Conditional Independence Graph Learning From Multi-Attribute Gaussian Dependent Time Series
by: Tugnait, Jitendra K.
Published: (2025)

Time Weaver: A Conditional Time Series Generation Model
by: Narasimhan, Sai Shankar, et al.
Published: (2024)

Sensitivity Curve Maximization: Attacking Robust Aggregators in Distributed Learning
by: Schroth, Christian A., et al.
Published: (2024)

Sensor Response-Time Reduction using Long-Short Term Memory Network Forecasting
by: Ward, Simon J., et al.
Published: (2024)

Beam Training in mmWave Vehicular Systems: Machine Learning for Decoupling Beam Selection
by: Kilinc, Ibrahim, et al.
Published: (2024)

Time is Not Enough: Time-Frequency based Explanation for Time-Series Black-Box Models
by: Chung, Hyunseung, et al.
Published: (2024)

Conformal Inference for Time Series over Graphs
by: Dua, Sonakshi, et al.
Published: (2025)

Reinforcement Learning of Adaptive Acquisition Policies for Inverse Problems
by: Silvestri, Gianluigi, et al.
Published: (2024)

Homological Time Series Analysis of Sensor Signals from Power Plants
by: Melodia, Luciano, et al.
Published: (2021)

Non-invasive Neural Decoding in Source Reconstructed Brain Space
by: Gideoni, Yonatan, et al.
Published: (2024)

Predicting NOx emissions in Biochar Production Plants using Machine Learning
by: Köppel, Marius, et al.
Published: (2024)

DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature Reuse
by: Pei, Yan, et al.
Published: (2023)

An Unsupervised Approach for Periodic Source Detection in Time Series
by: Demirel, Berken Utku, et al.
Published: (2024)

Edge-Optimized Deep Learning & Pattern Recognition Techniques for Non-Intrusive Load Monitoring of Energy Time Series
by: Athanasoulias, Sotirios
Published: (2025)

Augmentation of EEG and ECG Time Series for Deep Learning Applications: Integrating Changepoint Detection into the iAAFT Surrogates
by: Moutonnet, Nina, et al.
Published: (2025)

Three-Way Emotion Classification of EEG-based Signals using Machine Learning
by: Purwar, Ashna, et al.
Published: (2026)

An Objective Performance Evaluation of the LSTM Networks in Time Series Classification
by: Sunil, Sooraj, et al.
Published: (2026)

MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer
by: Han, Wenyong, et al.
Published: (2024)

A Network Science Approach to Granular Time Series Segmentation
by: Kesić, Ivana, et al.
Published: (2025)

Diffraction and Scattering Aware Radio Map and Environment Reconstruction using Geometry Model-Assisted Deep Learning
by: Chen, Wangqian, et al.
Published: (2024)

Deep Learning-Assisted Parallel Interference Cancellation for Grant-Free NOMA in Machine-Type Communication
by: Oh, Yongjeong, et al.
Published: (2024)

Task- and Metric-Specific Signal Quality Indices for Medical Time Series
by: Haidamous, Jad, et al.
Published: (2026)

Accelerometer-Based Multivariate Time-Series Dataset for Calf Behavior Classification
by: Dissanayake, Oshana, et al.
Published: (2024)

SPIRIT: Low Power Seizure Prediction using Unsupervised Online-Learning and Zoom Analog Frontends
by: Pandey, Aviral, et al.
Published: (2024)

Evaluating ROCKET and Catch22 features for calf behaviour classification from accelerometer data using Machine Learning models
by: Dissanayake, Oshana, et al.
Published: (2024)

Time-Vertex Machine Learning for Optimal Sensor Placement in Temporal Graph Signals: Applications in Structural Health Monitoring
by: Niresi, Keivan Faghih, et al.
Published: (2025)

GenTS: A Comprehensive Benchmark Library for Generative Time Series Models
by: Wang, Chenxi, et al.
Published: (2026)

Highly Efficient Direct Analytics on Semantic-aware Time Series Data Compression
by: Sun, Guoyou, et al.
Published: (2025)

Solving Inverse Problems with Model Mismatch using Untrained Neural Networks within Model-based Architectures
by: Guan, Peimeng, et al.
Published: (2024)

Analyzing Brain Activity During Learning Tasks with EEG and Machine Learning
by: Cho, Ryan, et al.
Published: (2024)