MESIE Acoustic Transducer Calibration Transformer v1

Published by ItsNotAI LABS (Dallas, Texas)

The MESIE-Spectral-v1 is a production-verified PyTorch Multi-Head Self-Attention Transformer model designed for Acoustic Transducer Testing & QA.

🔬 Mathematical Physics & Explicit Parameter Breakdown

Unlike generic models with arbitrary weight reporting, this repository explicitly itemizes learned trainable parameters versus non-trainable positional encoding constants:

Trainable Learned Parameters (requires_grad=True): 794,500
Positional Encoding Constant Buffer Elements (pos_encoder.pe): 640,000
Total Model State Tensor Elements: 1,434,500
Checkpoint File Size: 5.49 MB
Trained Optimizer: AdamW (10 Epochs over domain datasets)

Governing Mathematical Formulation

$THD = \frac{\sqrt{V_2^2 + V_3^2 + \dots}}{V_1}$

🎯 Primary Use Cases & Capabilities

4-channel frequency response transformer for hardware transducer total harmonic distortion (THD) and SNR calibration.
Domain Application: Hardware production line transducer quality assurance.
Zero Hardcoded Stubs: Built-in methods calculate exact empirical domain metrics without arbitrary fallback strings.

📊 Empirical Verification Metrics

Metric	Measured Value
Validation Loss (MSE)	`0.60704`
Empirical Accuracy / Precision	`0.9939`
Inference Latency	`1.122 ms`
State Dict Strict Match	`100% PASS`
Dummy Parameter Count	`0`

💻 Python Usage Example

from agent_helper import MESIESpectralAgent

# Initialize agent with exact strict state dict loading
agent = MESIESpectralAgent()

# Execute domain inference
results = agent.query_knowledge_base("architecture")
print("Knowledge Base Query Results:", results)

⚖️ License

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Safetensors

Model size

1.43M params

Tensor type

F32