Search Results for author:
Found 3 papers, 0 papers with code
Tree-Based Learning on Amperometric Time Series Data Demonstrates High Accuracy for Classification
In fact, the transients between spikes and the trace baselines carry essential information for a successful classification, thereby strongly demonstrating that an effective feature representation of amperometric time series requires the full time series.
Spike-by-Spike Frequency Analysis of Amperometry Traces Provides Statistical Validation of Observations in the Time Domain
The Fast Fourier Transform (FFT) is a well-established computational tool that is commonly used to find the frequency components of a signal buried in noise.
Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows
Reasons for this are the large amount of degrees of freedom in realistic flows, the high requirements with respect to accuracy and error robustness, as well as open questions, such as the generalization capability of trained neural networks in such high-dimensional, physics-constrained scenarios.
