Pasteur Labs provides a Simulation Intelligence (SI) platform that combines AI, differentiable physics, and high‑performance computing to deliver end‑to‑end, multi‑scale simulation environments. The platform enables engineers and scientists to rapidly prototype, optimize, and validate concepts across energy, climate, defense, and industrial R&D by exposing gradients for AI‑driven optimization and supporting hardware‑software co‑design. Integrated causal inference and cooperative AI tools let human‑machine teams explore large design spaces and accelerate development cycles from months to hours.
Funding
Funding not disclosed
Founders
Product
Problem
Engineers and scientists often face lengthy development cycles because existing simulation tools are limited to single-scale models, require extensive manual setup, and lack seamless integration with AI/ML workflows. This hampers rapid prototyping and scaling of innovations across domains such as energy, climate, defense, and industrial R&D.
Solution
Pasteur Labs offers a Simulation Intelligence (SI) platform that unifies AI, differentiable physics, and high-performance computing into an end-to-end, AI‑native environment. The platform provides flexible, multi‑scale simulation programs that can be co‑designed with hardware and machine‑programmed, enabling rapid iteration of hypotheses and solutions. Human‑machine teams use the in‑silico playgrounds to explore large design spaces, automatically generate gradients for optimization, and integrate causal inference and cooperative AI methods. Results are produced in hours rather than months, allowing engineers to validate concepts, scale prototypes, and deploy solutions across industrial, energy, and defense sectors.
Target Audience
Primary customers are computational engineers, scientists, and AI teams in industrial R&D, energy security, and defense organizations that require rapid, high‑fidelity multi‑scale simulation capabilities.
Features
- Patent‑pending end‑to‑end differentiable physics programs that expose gradients for AI‑driven optimization
- CAxML data interfaces that connect simulation pipelines directly with machine‑learning models and datasets
- HW‑SW co‑design framework enabling joint optimization of algorithms and underlying accelerator hardware
- Scalable multi‑physics simulation engine supporting energy, climate, socio‑economic, and agricultural domains
- Integrated causal inference and cooperative AI modules for open‑ended hypothesis generation
- Cloud‑ready architecture with APIs for seamless integration into existing engineering workflows