Precision that Learns.
Machine Learning that Answers to Physics
From field data to agentic intelligence: DDM Analytics builds Field-to-AI solutions for geoscience and engineering.
Advancing physics-informed, AI-driven predictive modeling for national-scale challenges in geoscience and engineering.
The physics fills the gaps the data leaves.
A structure's damped vibration, recorded at a handful of noisy points. Both models see the same measurements. The data-only model fits what it sees, then collapses beyond its samples; the physics-informed model carries the governing equation forward and holds the true response. Move the controls.
How this works
The system is a damped harmonic oscillator, m·ẍ + c·ẋ + k·x = 0, the
textbook model of a vibrating structure. Both models receive the same noisy samples, drawn only from the
training window. The data-only model is kernel ridge regression, a flexible
fit with no physics; away from its samples it decays to the baseline (zero). The physics-informed
model is constrained to the governing equation's solution family and fits its parameters to the same points,
so when the governing equation is known exactly it generalizes across the full record. Every curve and error here is computed live in your browser; nothing
is pre-rendered.
What we do
physics · agency · fieldPhysics-Informed Modeling
Machine learning constrained by the governing equations of the physical world, reliable where field data is sparse, noisy, or incomplete.
Agentic Intelligence
Autonomous reasoning agents and multi-agent systems that fuse multi-modal geospatial data and turn reliable predictions into reliable decisions.
Field-to-AI Infrastructure
GPU workstations for model development and a professional drone fleet with high-precision sensors, from data acquisition to validated models.
Research focus areas
where the work pointsRegional liquefaction hazard mapping using physics-informed machine learning.
R.02Critical MineralsRare earth element exploration in greenfield and brownfield settings, supported by physics-informed neural networks and agentic AI workflows.
R.03Legacy DataConverting historical field and operational records into analysis-ready datasets for predictive modeling.
Who we are
built by practitionersDDM Analytics is a research and development company built by practicing scientists and engineers who work as one team. Founded in Georgia in 2025, we combine production-grade data analytics with research-grade, physics-informed machine learning, and build toward Field-to-AI solutions powered by agentic intelligence. The practice is led by a licensed Professional Engineer (Georgia, North Carolina, and Tennessee) holding a PhD, so our research carries through to engineering work we can sign and stand behind.
Our clients
teams who work the ground
Start a conversation.
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