Navigating Uncertainties in Machine Learning for Structural Dynamics: A Comprehensive Survey of Probabilistic and Non-Probabilistic Approaches in Forward and Inverse Problems
cs.LG, math.DS
Submitted: 2024-08-16
Updated: 2025-10-19
Terminology
Sources
- A Survey on Uncertainty Quantification Methods for Deep Learning
- Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
- Deep Sub-Ensembles for Fast Uncertainty Estimation in Image Classification
- Neural Extended Kalman Filters for Learning and Predicting Dynamics of Structural Systems
- Auto-Encoding Variational Bayes
- Deep Bayesian Active Learning, A Brief Survey on Recent Advances
- Deep Gaussian Processes: A Survey
- Improving neural networks by preventing co-adaptation of feature detectors
- A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
- Bayesian Recurrent Neural Networks
- On Fast Dropout and its Applicability to Recurrent Networks
- Bayesian Transfer Learning
- Deep Bayesian U-Nets for Efficient, Robust and Reliable Post-Disaster Damage Localization
- Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference
- Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting
- Adam: A Method for Stochastic Optimization
- Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration
- A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning
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