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Found 15 papers, 9 papers with code
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation
While AD is typically treated as an unsupervised learning task due to the high cost of label annotation, it is more practical to assume access to a small set of labeled anomaly samples from domain experts, as is the case for semi-supervised anomaly detection.
Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applications
The intersection of physics and machine learning has given rise to the physics-enhanced machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the individual shortcomings of data- or physics-only methods.
SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization
In real-world scenarios, achieving domain generalization (DG) presents significant challenges as models are required to generalize to unknown target distributions.
Knowledge Engineering for Wind Energy
It presents the main concepts underpinning Knowledge-Based Systems and summarises previous work in the areas of knowledge engineering and knowledge representation in a manner that is relevant and accessible to domain experts.
POMDP inference and robust solution via deep reinforcement learning: An application to railway optimal maintenance
The POMDP with uncertain parameters is then solved via deep RL techniques with the parameter distributions incorporated into the solution via domain randomization, in order to develop solutions that are robust to model uncertainty.
Bridging POMDPs and Bayesian decision making for robust maintenance planning under model uncertainty: An application to railway systems
We present a framework to estimate POMDP transition and observation model parameters directly from available data, via Markov Chain Monte Carlo (MCMC) sampling of a Hidden Markov Model (HMM) conditioned on actions.
Neural Extended Kalman Filters for Learning and Predicting Dynamics of Structural Systems
Typically, conventional variational inference models are parameterized by neural networks independent of the latent dynamics models.
Vold-Kalman Filter Order tracking of Axle Box Accelerations for Railway Stiffness Assessment
Intelligent data-driven monitoring procedures hold enormous potential for ensuring safe operation and optimal management of the railway infrastructure in the face of increasing demands on cost and efficiency.
Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures
In the scope of physics-informed machine learning, this paper proposes a framework -- termed Neural Modal ODEs -- to integrate physics-based modeling with deep learning for modeling the dynamics of monitored and high-dimensional engineered systems.
Which Model to Trust: Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms for Continuous Control Tasks
The main contribution of this work lies precisely in assessing the model influence on the performance of RL algorithms.
Physics-guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertainty
To address this, we bridge physics-based state space models with Deep Markov Models, thus delivering a hybrid modeling framework for unsupervised learning and identification of nonlinear dynamical systems.
Relational VAE: A Continuous Latent Variable Model for Graph Structured Data
Graph Networks (GNs) enable the fusion of prior knowledge and relational reasoning with flexible function approximations.
Value of information from vibration-based structural health monitoring extracted via Bayesian model updating
We quantify this value by adaptation of the Bayesian decision analysis framework.
Applications Systems and Control Systems and Control
Foundations of Population-Based SHM, Part IV: The Geometry of Spaces of Structures and their Feature Spaces
One of the requirements of the population-based approach to Structural Health Monitoring (SHM) proposed in the earlier papers in this sequence, is that structures be represented by points in an abstract space.
Remaining Useful Life Estimation Under Uncertainty with Causal GraphNets
In this work, a novel approach for the construction and training of time series models is presented that deals with the problem of learning on large time series with non-equispaced observations, which at the same time may possess features of interest that span multiple scales.
