INTERACTIVE EXPERIENCES

Live Physics-Informed Demos

Experience the core value proposition in seconds. These educational proxies demonstrate residual-based trust and physics-constrained optimization — the foundation of our production systems.

Enterprise Cyber-Physical Ontology Simulator

v8.0 Thesis-Aligned

CIM + ThreMA Cross-Domain Ontology • PINN • RL Security Agent • IEEE 9-Bus Cyber Testbed • Signal-to-Noise Analytics

High AI compute surge on Bus 1. BESS supports peak.

38 MW
Discharge
0.35
Higher = more realistic variance

System Parameters — Editable in Real Time (CIM + ThreMA Enhanced)

Modify loads, generation, line parameters, or cyber vulnerability scores. Changes propagate to all analyses including ontology consistency and ThreMA threat modeling.

BUSES (4-Bus AI Data Center Test System) — Thesis Baseline
Bus 0 – Grid Tie (Slack)
Slack bus • Base 138 kV • Cyber Vuln: 15%
V = 1.000 pu
Load (MW)
Generation (MW)
ThreMA Protection: ACTIVE
Bus 1 – AI Data Center (PQ)
PQ bus • Base 138 kV • Cyber Vuln: 78%
V = 0.985 pu
Load (MW)
Generation (MW)
ThreMA Protection: VULNERABLE
Bus 2 – Solar + BESS (PV)
PV bus • Base 138 kV • Cyber Vuln: 42%
V = 1.015 pu
Load (MW)
Generation (MW)
ThreMA Protection: ACTIVE
Bus 3 – Industrial Load (PQ)
PQ bus • Base 138 kV • Cyber Vuln: 55%
V = 0.978 pu
Load (MW)
Generation (MW)
ThreMA Protection: VULNERABLE
TRANSMISSION LINES — CIM Connectivity + ThreMA Cyber Risk
LineFrom → ToReactance (pu)Rating (MW)Cyber RiskCurrent Flow
L0Bus 0 → Bus 118065%0.0 MW (0%)
L1Bus 0 → Bus 215038%0.0 MW (0%)
L2Bus 1 → Bus 212072%0.0 MW (0%)
L3Bus 1 → Bus 310081%0.0 MW (0%)
L4Bus 2 → Bus 311049%0.0 MW (0%)
Enterprise Cyber-Physical Ontology Simulator v8.0
Thesis-Aligned Edition • CIM + ThreMA + PINN + RL • Grimaldi 2025 Validation
5/19/2026, 8:42:26 PM
Executive Summary — AI Training Ramp-up + Cyber Analysis

Enhanced v8.0 report with full cross-domain ontology (CIM-ThreMA), attack forensics, RL policy, and loss landscape. All results generated from client-side physics-informed + ontology-validated DC power flow surrogate matching the IEEE 9-Bus validation in Grimaldi (2025) Master Thesis.

TRUST SCORE
0.0%
ONTOLOGY CONSISTENCY
0.0%
THREMA THREAT
0.0%
CROSS-DOMAIN SNR
0.0 dB
Generated for evaluation purposes. Production workloads use hardened JAX library + real-time digital twin + full OWL ontology. © 2026 Physics-Informed Systems — Thesis-Aligned Cyber-Physical Edition. Reference: Grimaldi, V. (2025). Data Modeling in a Cross-domain Ontology for Cyber Intelligence in Smart-Grids Using Reinforcement Learning. RWTH Aachen University.

Coming Soon in Platform

  • • Full AC Power Flow PINN with JAX (multi-bus, contingency analysis)
  • • Physics-Informed BESS + Data Center Co-optimization Dashboard
  • • Real-time State Estimation with Noisy Measurements
  • • Physics-Constrained Multi-Agent RL for Inverter Control
  • • Hybrid Digital Twin Builder (upload topology → instant surrogate)

All demos run client-side for instant feedback. Production workloads use our hardened Python/JAX library + hosted inference.

Ready for Production?

These interactive tools are designed to de-risk evaluation. The same principles power our enterprise APIs and managed digital twins.

View Enterprise Plans & API AccessOr request a custom pilot with your grid model →