Machine Learning
ML Models
Neural networks and statistical models trained to decode structural information from spectral measurements. Each model targets a different aspect of the signal-to-structure reconstruction pipeline.
Learns compressed latent representations of spectral signatures. Trained on boundary measurement data to encode the structural fingerprint of physical systems into a low-dimensional manifold.
Parameters
12,400
epochs
0.0037
loss
32
latentDim
Convolutional neural network that classifies raw signal waveforms by material composition and structural topology. Operates on windowed time-series segments with multi-scale feature extraction.
Parameters
8,200
epochs
0.0891
loss
12
classes
Ensemble-based anomaly detection system that identifies structural irregularities from spectral deviation patterns. Uses isolation forest with spectral feature engineering.
Parameters
200
estimators
0.05
contamination
48
features
NOTE: All models shown are in active development. Accuracy metrics reflect performance on held-out test sets from laboratory experiments. Production deployment requires additional validation against real-world boundary conditions.