Research
TechOtsu DeepInsight
Physics-informed computation for research teams
DeepInsight deploys small, physics-aware models that accelerate the numerical computations your research team already runs — orbital dynamics, spectroscopic analysis, atmospheric retrieval — without replacing your existing tools or workflows.
The Problem
Why this exists
Research teams spend 60-80% of computation time on tasks that are structurally repetitive: integrating the same type of equations, fitting the same types of models, running the same pipelines with different data. Classical numerical methods are accurate but slow at scale.
How It Works
Three steps, start to finish
1
We identify the computation bottlenecks in your existing research pipeline
2
We train physics-constrained surrogate models on your domain's equations and data
3
The surrogate drops in alongside your existing tools — same inputs, same outputs, 10-100× faster
Key Features
What you get
Physics-conserving — models respect energy conservation, symmetry laws, and domain constraints
Runs on your own infrastructure — no data sharing required
Compatible with Python, MATLAB, and C++ pipelines
Benchmarked against classical solvers with published accuracy metrics
Covers orbital mechanics, exoplanet characterization, galactic dynamics, spectroscopy
Custom training for your specific research domain
Who It's For
University research groups, national labs, and private research institutes working with large astronomical datasets, astrodynamics simulations, or physics-heavy numerical pipelines.