Physics-Informed AI
for Power Systems,
Proven by Reproduction.
An open-source library that solves power flow with neural networks constrained by the governing physics — validated against exact solvers on the IEEE 9-bus benchmark, with every number reproducible by a single command.
Power systems can't trust a black box
A model whose predictions can violate Kirchhoff's laws is hard to trust in critical infrastructure. Embedding the physics as a loss term makes compliance measurable.
Black-box predictions are hard to verify
If a forecast can quietly break the power-balance equations, an operator has no principled way to bound its error. That is a real barrier to adoption.
Physics is free supervision
The governing equations act as a strong inductive bias: faster convergence, better generalization from limited data, and a residual you can actually measure.
Residuals make trust quantitative
A physics-violation residual is a number you can check. We report it, and we show a controlled ablation where the physics-informed model violates it far less than a black box.
Interactive DC Power-Flow Explorer
An honest, in-browser tool that runs the exact DC power-flow solve on the real IEEE 9-bus network — the same physics the validated PINN learns to respect. A teaching aid, distinct from the reproducible results below.
Open the explorerA Power-Flow Library You Can Run Today
DC and AC power flow on the IEEE 9-bus benchmark, validated against analytical and Newton-Raphson ground truth • N-1 contingency • a controlled PINN-vs-black-box ablation • 25 passing tests • reproducible reports in Markdown / JSON / TXT / PDF. PyTorch.
Install the open-source library and regenerate every validated number locally — the PINN training, the analytical comparison, the N-1 sweep, and the full report. Nothing hidden, nothing hosted required.
See the reproducible resultsCited Research Background
We cite related academic work on ontologies for smart-grid cyber intelligence. It is background — distinct from, and not a claim of, the reproducible power-flow validation in this repository, which is our own work.
Data Modeling in a Cross-domain Ontology for Cyber Intelligence in Smart-Grids Using Reinforcement Learning
The thesis develops a methodology mapping the Common Information Model (CIM) to a cybersecurity framework for smart grids. Its results are the author's own contribution. We reference it as related background; it is not implemented in, or validated by, this repository.
Request the publication referenceWe Don't Just Claim Physics-Informed. We Prove It.
Our open-core PINN solves DC and AC power flow on the standard IEEE 9-bus benchmark, validated against the exact analytical and Newton-Raphson solutions. Every number below is reproducible with a single command.
The command trains the PINN on the IEEE 9-bus system, compares every bus angle against the exact DC power-flow solution, and regenerates the full validation report and convergence plot. Deterministic, seed-fixed, auditable.
View the full validation report on GitHubA Validation Report on Your System
A pilot produces one concrete deliverable: the same rigorous, reproducible report you see below — run on your network instead of the benchmark. Here is the exact format, so there are no surprises.
Inside the report
- 01 Ground-truth solution for your case (analytical / Newton-Raphson), verified to machine precision
- 02 Physics-informed model trained and measured against that ground truth, with per-bus error
- 03 N-1 contingency findings: which single-element losses your network survives
- 04 A controlled physics-violation comparison vs. an unconstrained baseline
- 05 The code to regenerate every number yourself — you keep it
See the sample
The IEEE 9-bus benchmark report — the exact deliverable format, in four open formats. Inspect it before you ever talk to us.
From Open-Source Library to a Scoped Pilot
Open Core + Python Library
- • PyTorch physics-residual training loops (DC and AC power flow)
- • Analytical DC solver + Newton-Raphson AC solver as exact ground truth
- • N-1 single-line-outage contingency sweep on IEEE 9-bus
- • Controlled PINN-vs-black-box ablation (physics-violation metric)
- • One-command reproducible reports: Markdown / JSON / TXT / PDF + convergence plot
Why a Physics-Informed Approach Earns Trust
A measurable physics-violation residual gives a quantitative trust signal — the kind of evidence engineers, auditors, and reviewers can check rather than take on faith.
Embedding the governing equations as a loss term lets the model generalize from limited data, which matters where labeled operational data is scarce or expensive.
Every published number is seed-fixed and regenerated by a single command, so a reviewer can confirm the claims independently before any conversation.
What a Pilot Actually Involves
Reproducible physics-informed
power flow. See it for yourself.
Start with the open-source library and the validation report. When you want it run on your own system, we scope a pilot.
OPEN CORE AVAILABLE NOW • PILOTS SCOPED INDIVIDUALLY
Upcoming Events
View all →- Webinar
Physics-Verified AI for Grid Operations: Live Demo
Wednesday, July 15, 2026 · 1:00 PM ET / 18:00 BST
See PINN-based power flow run live against a standard Newton-Raphson solver.
- Office HoursComing soon
Hybrid Digital Twin — Open Office Hours
Wednesday, July 22, 2026 · 11:00 AM ET
Bring your network model. We'll wire it to a self-calibrating twin.