We build physics-guaranteed ML you can drop straight into real control loops — open source at the core, with a managed layer on top.
An open-source inverse PINN engine that takes governing equations and noisy sensor data, discovers unknown physical parameters in real time, and outputs physics-guaranteed dynamics models that drop straight into MPC, RL, and state-estimation pipelines.
A coordination platform built on Hierarchical Federated Multi-Agent RL with PINN integration. It enforces physical limits with embedded electrochemical and thermal models, estimates hidden asset health through inverse physics, and preserves data privacy — augmenting existing VPP infrastructure rather than replacing it.
Follow the research, ship a PR, or just argue about loss functions in the community.
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