Detect anomalies in data using GPU-accelerated wave physics simulation. Fully stateless — send training data (normal examples) and test data (samples to check) in ONE call. Returns per-sample anomaly scores, confidence levels, and the top features explaining WHY each anomaly was flagged. Works on any data type: JSON objects, numbers, text, time series, arrays. No separate training step required.

Example: to check if server metrics are anomalous, send 3-5 normal readings as training, and the suspect readings as test.

Detect anomalies in time-series data using GPU-accelerated wave physics simulation. Send a flat array of numeric values and a window size. The tool automatically creates overlapping windows, uses the first N as training (normal baseline), and scores the remaining windows as test samples. Returns per-window anomaly scores, confidence, and p-values.

Example: send 100 CPU-usage readings with window_size=10. The first 5 windows become training, the rest are tested.

Check WaveGuard API health, GPU availability, version, and engine status. No authentication required. Use this to verify the service is running before scanning.