We build Physics-Informed Neural Networks and verification tooling that show their work — real execution, real residuals, real evidence, never a confidence number pulled from nowhere.
Four working systems, not slide decks. Click through and run them yourself.
Submit a physics problem, watch it execute, and get a per-check evidence breakdown — guardrail, convergence, and more — with the real number and source behind each one, plus a downloadable PDF report.
Run VeriPhysics →A trained neural surrogate predicting heatsink temperature in microseconds, checked every time against a real independent 3D finite-element solve.
Run the surrogate →A real-time river digital twin, calibrated live on a real US river gauge's own recent discharge data, forecasting behavior and checked against live data as it arrives.
Watch the live twin →Bring a real engineering problem. We test it against Physics AI, for free, in exchange for feedback — no sales pitch, one real experiment.
Bring your problem →Every number below came from an actual run, not an estimate.
The foundation is public, free, and on PyPI — real repositories, real releases, not vaporware.
The core engine — PINN/FNO/DeepONet/GNN architectures, solvers, and digital twin tooling. Everything else here is built on top of it.
A lightweight, standalone framework for building PINNs from symbolic PDE definitions (SymPy) with automatic differentiation in PyTorch — for anyone who wants PINNs without the whole platform.
A general-purpose agentic framework (ELM AI Agentic Framework) — an earlier-stage project, not yet wired into the rest of the PINNeAPPle story.
This is the honest split: the 3 repos above are genuinely open source. The demos in "Live demos" (VeriPhysics, the surrogate, the digital twin) run on top of this engine but are commercial products, not open-source releases.
Founder of PINNeAPPle Labs, building physics-informed machine learning that has to show its work — execution, verification, and evidence as the product, not an afterthought.