1 citations · 1 across the 2 of their papers we have counts for
5 papers
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…
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…
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…
Learning solutions of parametric Navier-Stokes with physics-informed neural networks
M. Naderibeni, M. J. T. Reinders, L. Wu +1
We leverage Physics-Informed Neural Networks (PINNs) to learn solution functions of parametric Navier-Stokes Equations (NSE). Our proposed approach results in a feasible optimizati…
Improving performance of heart rate time series classification by grouping subjects
Michael Beekhuizen, Arman Naseri, David Tax +2
Unlike the more commonly analyzed ECG or PPG data for activity classification, heart rate time series data is less detailed, often noisier and can contain missing data points. Usin…