Mathematical intelligence paradigm

Operator Discovery

Discovers governing equations from raw data across any scientific or engineering domain. No assumed models. No training labels. No neural architectures.

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Not ML | Not deep learning | Not PINNs

What it is

Operator Discovery (OD) is a mathematical intelligence paradigm developed by DavisAI Systems. It discovers governing equations from raw data across any scientific or engineering domain without assumed models, training labels, or neural architectures.

How it differs

What OD is not

× Not machine learning (no training labels required)

× Not deep learning (no neural architecture)

× Not physics-informed neural networks (no assumed equations)

× Not a black box (outputs human-readable math)

What OD is

✓ Data-driven equation discovery

✓ Tests candidates against data, discards what fails

✓ Produces human-readable mathematical equations

✓ Results can be independently verified

Where it works

Validated across multiple scientific domains with published results.

Quantum Mechanics

Spin-boson systems: 1,540 two-bath recoveries in 42.3 seconds, R-squared 0.96. Validated across two orders of magnitude in coupling strength.

Quantum Decoherence

Lindblad master equation derivation from raw measurement statistics. Coherence enhancement up to 147x. Validated on IBM Quantum hardware. Published results.

Adversarial Robustness

OLAD: Walking-to-Standing transfer accuracy of 99-100 percent across neural architectures. Zero-shot generalization without retraining.

What it produces

Human-readable mathematical equations that can be independently verified. Not a black box. Not a probability distribution. Actual equations. When OD recovers the amplitude damping channel from quantum measurement data, the output is G(p) = exp(-gamma dt) p + (1 - exp(-gamma dt)). That equation can be checked against the known Lindblad solution.

What it is not

Not a chatbot. Not generative AI. Not a foundation model. OD is a discovery engine that outputs math, not text. It does not generate responses, predict tokens, or produce natural language. It discovers the mathematical relationships that govern physical and engineered systems.