What’s New:
November 2024
RNET was awarded a Army Phase I STTR to develop a scalable, robust framework for reduced-order modeling (ROM) in hypersonic boundary layer applications.
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August 2024
RNET has recently been awarded a NASA Phase I STTR to an autonomous storm tracking and control for space based lightning sensors.
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July 2024
RNET has recently been awarded a NASA Phase II SBIR to develop a Rapid Data Analytics Platform using machine learning uses a novel dimensional reduction algorithm.
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High‑performance computing, scalable scientific AI, and high‑fidelity physics modeling for government, academia, and industry. We build advanced algorithms, streaming AI pipelines, and next‑generation tools for extreme‑scale scientific and engineering workloads.
What We Do
- High‑Performance Computing (GPU acceleration, distributed scaling, performance portability)
- Scalable Streaming AI (end‑to‑end training on multi‑terabyte scientific, simulation, satellite, and pathology‑scale imagery datasets)
- Scientific AI & Machine Learning (surrogate modeling, UQ, large‑data training pipelines)
- Fidelis: Scientific data compression & compressed‑domain analytics
- High‑Fidelity Computational Physics (RANS/LES/DNS, multiphysics, multiphase, hypersonics)
- Fluid‑Structure Interactions (aerothermal and aeroelastic modeling)
- Tools & Workflows (in‑situ V&V, cloud‑based workflows, simulation support tools)
Collaborate With Us
We work with universities, national laboratories, government agencies, and industry through collaborative R&D, CRADAs, cooperative agreements, and competitive federal research programs.
Contact us — we’re always open to new collaborations.