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Found 21 papers, 2 papers with code
On the Convergence of Locally Adaptive and Scalable Diffusion-Based Sampling Methods for Deep Bayesian Neural Network Posteriors
Achieving robust uncertainty quantification for deep neural networks represents an important requirement in many real-world applications of deep learning such as medical imaging where it is necessary to assess the reliability of a neural network's prediction.
Position Paper on Materials Design -- A Modern Approach
In this way, the most complex physical relationships can be considered and quickly described.
Diagnosis driven Anomaly Detection for CPS
In Cyber-Physical Systems (CPS) research, anomaly detection (detecting abnormal behavior) and diagnosis (identifying the underlying root cause) are often treated as distinct, isolated tasks.
A Generative Neural Network Approach for 3D Multi-Criteria Design Generation and Optimization of an Engine Mount for an Unmanned Air Vehicle
For a neural network the functionalities are translated in conditions to a certain geometry.
Discret2Di -- Deep Learning based Discretization for Model-based Diagnosis
Consistency-based diagnosis is an established approach to diagnose technical applications, but suffers from significant modeling efforts, especially for dynamic multi-modal time series.
Using Autoencoders and AutoDiff to Reconstruct Missing Variables in a Set of Time Series
In this paper, a new approach to reconstruct missing variables in a set of time series is presented.
Graph Structural Residuals: A Learning Approach to Diagnosis
Traditional model-based diagnosis relies on constructing explicit system models, a process that can be laborious and expertise-demanding.
Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study
Deep learning (DL) models have seen increased attention for time series forecasting, yet the application on cyber-physical systems (CPS) is hindered by the lacking robustness of these methods.
A Diagnosis Algorithms for a Rotary Indexing Machine
Finally, we test this algorithm on a model of a rotary indexing machine, demonstrating its effectiveness in identifying faults and their root causes.
A Cross-Frequency Protective Emblem: Protective Options for Medical Units and Wounded Soldiers in the Context of (fully) Autonomous Warfare
With regard to the technical deployment, possibilities are considered to address different sensors and to send signals out as resiliently as possible.
High Accuracy Uncertainty-Aware Interatomic Force Modeling with Equivariant Bayesian Neural Networks
Even though Bayesian neural networks offer a promising framework for modeling uncertainty, active learning and incorporating prior physical knowledge, few applications of them can be found in the context of interatomic force modeling.
Plan-Based Derivation of General Functional Structures in Product Design
In product design, a decomposition of the overall product function into a set of smaller, interacting functions is usually considered a crucial first step for any computer-supported design tool.
On a Uniform Causality Model for Industrial Automation
A promising approach to deal with this complexity is the concept of causality.
Problem examination for AI methods in product design
Early mappings of these concepts to AI solutions are sketched and verified using design examples.
A Research Agenda for AI Planning in the Field of Flexible Production Systems
Manufacturing companies face challenges when it comes to quickly adapting their production control to fluctuating demands or changing requirements.
Learning Physical Concepts in Cyber-Physical Systems: A Case Study
In Cyber-Physical Systems (CPS), ML can for example be used to optimize systems, to detect anomalies or to identify root causes of system failures.
Reconfiguring Hybrid Systems Using SAT
Instead, AI-based approaches are needed which leverage on a model of the non-faulty system and which search for a set of reconfiguration operations which will establish a valid behavior again.
A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata
Performing anomaly detection in hybrid systems is a challenging task since it requires analysis of timing behavior and mutual dependencies of both discrete and continuous signals.
LSTM for Model-Based Anomaly Detection in Cyber-Physical Systems
Anomaly detection is the task of detecting data which differs from the normal behaviour of a system in a given context.
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder
In this work, we focus on the nonlinear autoencoder (AE) as a DR/AD approach.
The DigitalTwin from an Artificial Intelligence Perspective
Services for Cyber-Physical Systems based on Artificial Intelligence and Machine Learning require a virtual representation of the physical.
