3 papers
cs.CE2026
Accelerated and data-efficient flow prediction in stirred tanks via physics-informed learning
Mahdi Naderibeni, Liang Wu, David M. J. Tax
The simulation of fluid flows is computationally expensive due to the complexity of its governing partial differential equations. Machine learning models offer a potential surrogat…
cs.LG2024
PATE: Proximity-Aware Time series anomaly Evaluation
Ramin Ghorbani, Marcel J. T. Reinders, David M. J. Tax
Evaluating anomaly detection algorithms in time series data is critical as inaccuracies can lead to flawed decision-making in various domains where real-time analytics and data-dri…
cs.LG2024
RESTAD: REconstruction and Similarity based Transformer for time series Anomaly Detection
Ramin Ghorbani, Marcel J. T. Reinders, David M. J. Tax
Anomaly detection in time series data is crucial across various domains. The scarcity of labeled data for such tasks has increased the attention towards unsupervised learning metho…