PHYSICAL INFRASTRUCTURE → DATA → MODELS → INTELLIGENCE → ASSURANCE → ACTION
Electrical Engineering • Critical Infrastructure • Digital Twins • Physics-Informed AI • Industrial Systems • Robotics • OT Security
Software-defined engineering for systems that exist in the physical world.
I design and build software-defined infrastructure for complex physical systems.
The physical layer is the starting point.
Electrical infrastructure, power systems, traction systems, industrial assets, machines and networks become the foundation on which software, telemetry, semantic models, simulation, artificial intelligence, cybersecurity and autonomous systems are built.
The central problem is not another isolated application.
It is integration.
flowchart LR
P[Physical Infrastructure]
E[Electrical / Industrial Systems]
T[Sensing + Telemetry]
D[Protocols + Data]
S[Semantic Models]
DT[Digital Twin]
SIM[Simulation]
AI[AI / ML / Optimization]
A[Policy + Assurance]
R[Automation / Robotics]
X[Physical Action]
P --> E --> T --> D --> S --> DT --> SIM --> AI --> A --> R --> X --> P
flowchart LR
A[PHYSICS] --> B[ELECTRICAL ENGINEERING] --> C[DATA + TELEMETRY] --> D[DIGITAL TWINS] --> E[SIMULATION] --> F[AI / ML] --> G[AGENTS] --> H[ASSURANCE] --> I[AUTOMATION] --> J[PHYSICAL SYSTEMS]
Electrical and physical reality provide the constraints; software provides the representation; data provides observability; AI provides intelligence; assurance governs action.
The portfolio is positioned around several of the most active technical directions now emerging across power-system engineering and critical infrastructure.
| Frontier | Current direction | Engineering implication |
|---|---|---|
| Grid Foundation Models | Large-scale AI models for power-system planning, scenario generation and decision support | Grid models are moving beyond isolated predictors toward reusable computational intelligence |
| Graph + Time-Series Grid AI | Models combining network topology, time-series behaviour and generative methods | Infrastructure AI increasingly needs both structure and dynamics |
| Agentic Grid Operations | AI agents are being tested for operator assistance, recommendations and workflow integration | Agents need simulation, policy boundaries, explainability and human oversight |
| Digital Substations | IEC 61850 continues evolving, including newer 2026 material and power-system modelling work | Interoperability and semantic infrastructure remain foundational |
| DER Intelligence | Storage, controllable loads, aggregations and distributed resources require richer machine-readable models | Grid software increasingly operates on fleets rather than individual assets |
| AI Assurance for Critical Infrastructure | Formal work is emerging around trustworthy AI, determinism, resilience and graceful degradation | "AI capability" is becoming inseparable from assurance architecture |
| Open Energy Infrastructure | Open-source projects are advancing from experimentation toward production and ecosystem integration | Interoperability becomes an engineering strategy rather than a documentation exercise |
Examples include DOE's 2026 GridFM 2.0 project targeting vastly higher grid-scenario throughput; DOE/LLNL's Stormbreaker testbed for LLM and agentic-AI evaluation in power/OT environments; GE Vernova's active Dynamic Grid Foundation Model project; LF Energy's AINETUS, Grid2Op and OpenGridFM activities; NIST's ongoing trustworthy-AI profile for critical infrastructure; and the 2026 IEC 61850 series release. DOE GridFM 2.0 · DOE Stormbreaker · GE Vernova DynaGridFM · LF Energy AINETUS · NIST Critical Infrastructure AI · IEC 61850:2026
Modern infrastructure is no longer divided cleanly into hardware and software.
An electrical asset can simultaneously be:
physical machine · power-system component · real-time system · OT endpoint · cybersecurity boundary · telemetry source · digital-twin object · ML dataset · agent environment · operational decision surface
The engineering objective is therefore:
flowchart LR
ASSET[Asset]
STATE[State]
MODEL[Model]
TWIN[Digital Twin]
INTEL[Intelligence]
ASSURE[Assurance]
ACTION[Action]
FEEDBACK[Measured Feedback]
ASSET --> STATE --> MODEL --> TWIN --> INTEL --> ASSURE --> ACTION --> FEEDBACK --> STATE
The loop is intentionally closed.
A useful platform should not stop at visualization.
It should be capable of:
observe → model → simulate → predict → optimize → assure → act → measure → learn
flowchart LR
subgraph P["PHYSICAL"]
PA[Physical Assets]
EE[Electrical Engineering]
OT[Industrial Networks]
end
subgraph D["DIGITAL"]
TEL[Telemetry]
DATA[Data]
SEM[Semantic Model]
DT[Digital Twin]
end
subgraph I["INTELLIGENCE"]
SIM[Simulation]
ML[Physics-Informed AI]
OPT[Optimization]
AG[Agents]
end
subgraph A["ASSURANCE"]
SEC[Cybersecurity]
POL[Policy]
VER[Verification]
RUN[Runtime Assurance]
end
subgraph O["OPERATION"]
EDGE[Edge]
CTRL[Control]
ROB[Robotics]
HUM[Human / Operator]
end
PA --> EE --> TEL --> DATA --> SEM --> DT
OT --> TEL
DT --> SIM
DT --> ML
DT --> OPT
DT --> AG
SIM --> SEC
ML --> POL
OPT --> VER
AG --> RUN
SEC --> EDGE
POL --> CTRL
VER --> ROB
RUN --> HUM
EDGE --> PA
CTRL --> PA
ROB --> PA
HUM --> PA
Grimaldi.ca is the strategic and visual surface.
GitHub is the engineering surface.
Together:
flowchart LR
WEB[GRIMALDI.CA<br/>Strategic / Visual]
GH[GITHUB<br/>Engineering]
R[Research]
S[Software]
SYS[Systems]
EN[Energy]
AI[Intelligence]
ROB[Robotics]
CI[Intelligent Infrastructure]
WEB --> GH
GH --> R
GH --> S
GH --> SYS
R --> EN
S --> AI
SYS --> ROB
EN --> CI
AI --> CI
ROB --> CI
flowchart LR
subgraph PHYSICAL["PHYSICAL DOMAIN"]
HV[High Voltage]
MV[Medium Voltage]
TP[Traction Power]
GRID[Power Networks]
IND[Industrial Assets]
ROB[Robotics]
end
subgraph DIGITAL["DIGITAL DOMAIN"]
TEL[Telemetry]
PROT[Protocols]
DATA[Data Platforms]
SEM[Semantic Models]
TWIN[Digital Twins]
SIM[Simulation]
end
subgraph INTEL["INTELLIGENCE"]
ML[Machine Learning]
PINN[Physics-Informed AI]
OPT[Optimization]
RL[Reinforcement Learning]
AG[AI Agents]
end
subgraph ASSURE["ASSURANCE"]
SEC[Cybersecurity]
VER[Verification]
OBS[Observability]
POL[Policy]
RT[Runtime Assurance]
end
subgraph ACTION["ACTION"]
EDGE[Edge]
AUTO[Automation]
CTRL[Control]
OP[Operator Systems]
end
HV --> TEL
MV --> TEL
TP --> TEL
GRID --> TEL
IND --> PROT
ROB --> TEL
TEL --> DATA
PROT --> DATA
DATA --> SEM
SEM --> TWIN
TWIN --> SIM
SIM --> ML
TWIN --> ML
ML --> OPT
OPT --> RL
RL --> AG
AG --> SEC
OPT --> VER
ML --> POL
SEC --> RT
VER --> RT
POL --> RT
RT --> EDGE
RT --> AUTO
RT --> CTRL
RT --> OP
The engineering surface is designed to connect with the broader open engineering ecosystem rather than exist as an isolated island.
LF Energy · GridAPPS-D · OpenEMS · PyPSA · pandapower · OpenDSS · HELICS
Grid2Op · OpenGridFM · AINETUS · SOGNO · Power Grid Model · PowSyBl · Dynawo · OperatorFabric
IEC 61850 · CIM · IEC 61968 · IEC 61970 · IEC 62351 · IEC 62443
HELICS · RTDS · OPAL-RT · MATLAB/Simulink · OMNeT++ · scientific Python
Digital Twins · Runtime Assurance · OT Security · Functional Safety · HIL · SIL · Edge Computing
The goal is not to duplicate established ecosystems.
It is to build the engineering layers that connect them.
flowchart LR
STD[Open Standards] --> MODEL[Open Models] --> SIM[Open Simulation] --> DATA[Open Data] --> SW[Open Software] --> INTEL[Intelligent Engineering]
LF Energy's 2026 ecosystem activity is particularly relevant here: AINETUS is explicitly designed to integrate with Grid2Op, OperatorFabric and SOGNO; Grid2Op and OpenGridFM moved toward Incubation; SEAPATH reached Graduation; and additional projects are expanding AI, DER interoperability and operational tooling.
Electrical infrastructure forms the physical foundation.
Domains
- High-voltage substations
- Medium-voltage systems
- Railway traction power
- 16.7 Hz traction networks
- Protection and automation
- Condition monitoring
- Predictive maintenance
- Asset health
- Distributed Energy Resources
- Grid flexibility
- Renewable integration
- Grid constraints
- Reliability and resilience
- Digital substations
- Operational telemetry
flowchart LR
ASSET[Electrical Asset] --> MODEL[Electrical Model] --> DATA[Digital Representation] --> OBS[Observable System] --> INTEL[Intelligent System]
Modern electrical-system software is increasingly moving toward:
digital substations → DER-aware semantic models → grid-forming / inverter-dominated systems → real-time operational data → AI-assisted planning and operations → validated decision support
IEC's 2026 IEC 61850 series includes new material such as IEC 61850-7-410:2026, while IEC 61850-7-420:2021 provides information models for DER and distribution automation.
Machine intelligence becomes more useful for physical systems when it understands the systems it is modelling.
Research surface
- Physics-Informed Neural Networks
- Neural Operators
- Fourier Neural Operators
- Scientific Machine Learning
- Hybrid Physics / ML
- State Estimation
- Surrogate Modelling
- Uncertainty Quantification
- Probabilistic Forecasting
- Distribution-Shift Detection
- Constrained Optimization
- Reinforcement Learning
- Multi-Agent Reinforcement Learning
- Neuro-Symbolic Systems
- Explainable AI
- Runtime Monitoring
- Safety Constraints
flowchart LR
PHYS[Physics] --> DATA[Observations] --> MODEL[Hybrid Model] --> PRED[Prediction] --> OPT[Optimization] --> DECISION[Decision]
AI should not replace engineering constraints.
AI should operate inside them.
A major current direction is the combination of:
foundation models + graph structure + time-series data + physical constraints + simulation + uncertainty + operator workflows
DOE's September 2026 GridFM 2.0 initiative is explicitly pursuing large-scale foundation-model approaches for grid planning and expansion, while an active GE Vernova/Georgia Tech/PNNL project is developing a Dynamic Grid Foundation Model using generative AI, time-series modelling and graph neural networks.
The interesting problem is not simply whether an AI system can reason.
It is whether the system can reason inside a governed engineering environment.
Required boundaries
- Explicit capabilities
- Tool boundaries
- Deterministic interfaces
- Authentication
- Authorization
- Auditability
- Observability
- Runtime assurance
- Policy enforcement
- Human-in-the-loop escalation
- Physical constraints
- Fail-safe behaviour
- Reproducibility
- Cryptographic provenance
flowchart LR
HUMAN[Human / Operator] --> AGENT[AI / LLM / Agent] --> POLICY[Policy / Capability Boundary] --> TOOL[Validated Tool Interface] --> PHYS[Physics / Safety Constraints] --> ASSURE[Runtime Assurance] --> ACTION[Industrial / Physical Action] --> OBS[Observability] --> HUMAN
Agentic AI is moving into dedicated critical-infrastructure test environments rather than remaining purely a conversational technology.
DOE and Lawrence Livermore's 2026 Stormbreaker testbed is specifically designed to evaluate LLMs and agentic AI in power-system and OT environments. LF Energy's AINETUS similarly places AI decision support inside an established simulation and operator ecosystem instead of treating the agent as a standalone application.
A digital twin should move beyond static visualization.
flowchart LR
STATE[Live State] --> TEL[Telemetry] --> TOPO[Topology] --> SEM[Semantic Model] --> PHYS[Physical Equations] --> SIM[Simulation] --> PRED[Prediction] --> OPT[Optimization] --> SCEN[Scenarios] --> OPS[Operator] --> ACTION[Controlled Action]
flowchart LR
MODEL[Digital Model] --> TWIN[Digital Twin] --> OP[Operational Twin] --> INTEL[Intelligent Twin] --> AGENT[Agentic Cyber-Physical System]
The strongest digital-twin architectures are converging with:
live operational state + communications + controls + simulation + AI + security + decision support
Digital-twin work in critical infrastructure is therefore increasingly about dynamic operational models rather than 3D visualization alone. DOE research programs and recent utility-AI initiatives reflect this shift.
Systems & Languages
Python · C · C++ · Rust · Go · Java · C# · Bash · PowerShell
Systems engineering · Embedded development · Scientific computing · Real-time software · Backend services · Automation · Data engineering · Simulation · AI/ML · Infrastructure tooling
Application & Platform Engineering
TypeScript · JavaScript · React · Next.js · Node.js · FastAPI · Flask · Django · HTML · CSS · Tailwind
REST APIs · Async services · Event-driven backends · WebSockets · Streaming · Distributed services · API gateways · Authentication · Authorization · Dashboards · Engineering control surfaces
AI / ML / Scientific Computing
PyTorch · TensorFlow · JAX · ONNX · NumPy · SciPy · Pandas · scikit-learn · OpenCV · Matplotlib · Plotly · Jupyter
Deep learning · Scientific ML · PINNs · Neural operators · Computer vision · Representation learning · Time-series · Forecasting · Anomaly detection · Reinforcement learning · Multi-agent systems · Optimization · Uncertainty quantification · Digital-twin surrogate models
Real-Time & Embedded
RTOS · Embedded Linux · C/C++ · Rust · Real-time scheduling · Deterministic execution · WCET analysis · Interrupt-driven systems · Memory safety · IPC · Device communication · Hardware interfaces · Signal processing · Edge inference · HIL · SIL · Functional safety · Runtime monitoring
flowchart LR
SPEED[Fast] --> LATENCY[Low Latency] --> DETERMINISM[Determinism] --> ASSURANCE[Operational Assurance]
Fast ≠ Deterministic · Low Latency ≠ Guaranteed Latency · AI Accuracy ≠ Operational Safety
| Technology | Engineering domain |
|---|---|
| IEC 61850 | Digital substations & protection |
| MMS | IEC 61850 client/server |
| GOOSE | Fast substation events |
| Sampled Values | Digital measurement streams |
| IEC 61850-90-x | Extended grid communication |
| DNP3 | Utility telemetry and control |
| Modbus | Industrial equipment |
| OPC UA | Industrial interoperability |
| MQTT | Telemetry |
| Sparkplug B | Industrial MQTT information model |
| NATS | High-performance messaging |
| Kafka | Distributed event streaming |
| AMQP / RabbitMQ | Message-oriented systems |
| CIM / IEC 61968 / IEC 61970 | Utility semantic modelling |
| HELICS | Energy-system co-simulation |
flowchart LR
ASSET[Asset] --> PROTOCOL[Protocol] --> SEMANTIC[Semantic Model] --> TWIN[Digital Twin] --> SIM[Simulation] --> AI[AI / Optimization] --> DECISION[Decision]
The objective is not protocol collection.
It is interoperability across heterogeneous infrastructure.
flowchart LR
C[Confidentiality] --> I[Integrity] --> A[Availability] --> S[Physical Safety] --> ST[System Stability]
Security surface
OT cybersecurity · Zero-trust architecture · Network segmentation · IAM · Secure remote access · PKI · Certificate management · Secure telemetry · Cryptographic signing · Supply-chain security · SBOM · Vulnerability management · Threat modelling · Security monitoring · Runtime protection · Incident response · Resilience engineering
IEC 62351 · IEC 62443 · NERC CIP · NIS2 · EU Cyber Resilience Act · MITRE ATT&CK for ICS
AI assurance for critical infrastructure is becoming a first-class engineering concern. NIST's 2026 Trustworthy AI in Critical Infrastructure work explicitly addresses AI systems operating at the intersection of AI, IT, OT, ICS, cybersecurity and physical infrastructure, including deterministic behaviour, explainability, graceful degradation and fail-safe operation.
flowchart LR
PHYSICAL[Physical Assets] --> EDGE[Edge<br/>RT / AI / OT] --> REGIONAL[Regional Services] --> CLOUD[Cloud<br/>Data / ML / AI] --> OPERATORS[Operators + Engineering] --> PHYSICAL
Docker · Kubernetes · Terraform · Ansible · Linux · AWS · Azure · GCP · NGINX
Containerization · Infrastructure as Code · GitOps · Edge deployments · Distributed services · Observability · Secrets management · Automated testing · Secure supply chains · Reproducible deployments
PostgreSQL · TimescaleDB · InfluxDB · Redis · MongoDB · Neo4j · Cassandra · Kafka · RabbitMQ · NATS
flowchart LR
EVENTS[Events] --> STREAMS[Streams] --> TIMESERIES[Time Series] --> GRAPH[Asset Graph] --> SEMANTICS[Semantic Layer] --> FEATURES[Real-Time Features] --> MODELS[Models] --> DECISIONS[Decisions]
Time-series databases · Event sourcing · Streaming architectures · Graph databases · Telemetry pipelines · Digital-thread architectures · Historical replay · Event correlation · Asset knowledge graphs · Real-time feature pipelines
flowchart LR
LOGS[Logs] --> METRICS[Metrics] --> TRACES[Traces] --> EVENTS[Events] --> TELEMETRY[Telemetry] --> MODELS[Model Predictions] --> AGENTS[Agent Actions] --> OPERATORS[Operator Decisions] --> OBS[Unified Observability]
Prometheus · Grafana · OpenTelemetry · Elasticsearch · Loki · distributed tracing · structured logging
The objective:
make system behaviour observable during operation and explainable after the fact.
Complex cyber-physical systems need environments where ideas can be tested before touching physical infrastructure.
flowchart LR
POWER[Power Simulation] --> NETWORK[Network Simulation] --> CPS[Cyber-Physical Simulation] --> AGENT[Agent Simulation] --> RL[RL Environment] --> HIL[Hardware-in-the-Loop] --> SIL[Software-in-the-Loop] --> SCENARIO[Scenario Generation] --> ADV[Adversarial Testing]
HELICS · OMNeT++ · RTDS · OPAL-RT · MATLAB/Simulink · Python scientific computing
Power-system simulation · Cyber-physical simulation · Network simulation · Agent-based simulation · RL environments · Digital twins · HIL · SIL · contingency analysis
Research and engineering around LiDAR-based perception and autonomous inspection.
LiDAR · Computer vision · Sensor fusion · Point clouds · SLAM · Localization · Object detection · 3D reconstruction · Uncertainty estimation · ROS 2 · Autonomous navigation · Path planning · Robotic inspection · Edge inference
flowchart LR
SENSOR[LiDAR / Cameras] --> FUSION[Sensor Fusion] --> PERCEPTION[Perception] --> LOCALIZATION[Localization] --> TWIN[3D / Digital Twin] --> PLANNING[Path / Action Planning] --> INSPECTION[Infrastructure Inspection]
Technology interests:
Three.js · WebGL · WebGPU · Unity · Unreal Engine · Blender · Plotly · Dash · React
flowchart LR
ASSET[Asset] --> STATE[Live State] --> 3D[3D View] --> TELEMETRY[Telemetry] --> SIM[Simulation] --> WHATIF[What-If Analysis] --> OPERATOR[Operator Interface]
Applications:
3D digital twins · Substation visualization · Asset visualization · Network topology · Live telemetry · Simulation playback · Operator dashboards · Spatial interfaces · Engineering visualization · Interactive what-if analysis
One of the largest infrastructure problems is not missing data.
It is missing shared meaning.
flowchart LR
ASSET[Physical Asset] --> TELEMETRY[Telemetry] --> PROTOCOL[Protocol] --> MODEL[Semantic Model] --> TWIN[Digital Twin] --> SIM[Simulation] --> AI[AI / Optimization] --> DECISION[Decision]
Relevant technologies:
CIM · IEC 61968 · IEC 61970 · IEC 61850 · knowledge graphs · ontologies · RDF · graph databases · semantic APIs
The target is a digital thread capable of surviving vendors, protocols and software generations.
| # | Principle | Meaning |
|---|---|---|
| 01 | Physics over assumptions | Known physical laws should constrain computational models |
| 02 | Determinism where it matters | Critical paths should have bounded behaviour where required |
| 03 | AI inside guardrails | Intelligence operates inside explicit boundaries |
| 04 | Observable by design | Systems should expose enough state to diagnose and govern them |
| 05 | Secure by architecture | Security is structural, not an afterthought |
| 06 | Open standards | Infrastructure should remain interoperable |
| 07 | Human accountability | Automation should increase capability without removing responsibility |
| 08 | Build for the physical world | Software must respect electrical, mechanical, thermal, temporal and safety constraints |
⚡ physics-informed
Physics-informed cyber-physical simulation and scientific-AI research environment.
Key areas
Physics-Informed Neural Networks · Neural operators · Cyber-physical simulation · CIM integration · Power-system modelling · Reinforcement learning · Adversarial scenarios · IEEE benchmark systems · Physics-constrained inference
Repository
https://github.com/iceccarelli/physics-informed
Live environment
🧠 NeuralBridge
A research and engineering direction focused on deterministic middleware between intelligent software and cyber-physical environments.
flowchart LR
HUMAN[Human] --> AGENT[AI / LLM / Agent] --> POLICY[Policy Layer] --> VALIDATE[Validation] --> ASSURE[Runtime Assurance] --> PHYSICAL[Physical System]
Repository
⚡ GridOS
A next-generation digital operating environment for intelligent electrical infrastructure.
High-voltage telemetry · Digital twins · Grid observability · DER coordination · Real-time simulation · Physics-informed intelligence · Operator interfaces · Autonomous decision support · Cyber-physical resilience
Repository
🔋 DERIM
Distributed Energy Resource Intelligence Middleware.
DER orchestration · Industrial protocols · Grid flexibility · Distributed optimization · Multi-agent coordination · Physics-aware control · Grid services
Repository
🦾 robot-lidar-fusion
Research and engineering around LiDAR-based perception and sensor fusion for autonomous inspection.
LiDAR · Sensor fusion · Point-cloud processing · Computer vision · Uncertainty · Autonomous inspection · Robotics · Safety-aware action planning
Repository
GridOS — digital-twin and intelligent infrastructure engineering environment
The repositories are not intended to be isolated software projects.
They form a connected research and engineering surface.
flowchart LR
EE[Electrical Engineering]
EE --> PHY[physics-informed]
EE --> GRID[GridOS]
EE --> DER[DERIM]
EE --> ROB[robot-lidar-fusion]
PHY --> TWIN[Digital Twin]
GRID --> TWIN
DER --> TWIN
ROB --> TWIN
TWIN --> SIM[Simulation]
TWIN --> AI[Physics-Informed AI]
SIM --> AI
AI --> NB[NeuralBridge]
NB --> AGENTS[Agentic Systems]
AGENTS --> ASSURE[Runtime Assurance]
ASSURE --> CONTROL[Controlled Action]
CONTROL --> EE
flowchart LR
PHYSICAL[Physical Domain] --> MODEL[Model] --> SIMULATE[Simulate] --> INTELLIGENCE[Intelligence] --> ASSURE[Assure] --> ACT[Act] --> MEASURE[Measure] --> LEARN[Learn] --> PHYSICAL
The portfolio is deliberately designed to connect to established technical foundations.
flowchart LR
subgraph OPEN["OPEN ENGINEERING ECOSYSTEM"]
CIM[CIM]
IEC[IEC 61850]
LF[LF Energy]
GRIDAPPS[GridAPPS-D]
OPENEMS[OpenEMS]
PYPSA[PyPSA]
PP[pandapower]
ODSS[OpenDSS]
HELICS[HELICS]
end
subgraph GRIMALDI["ENGINEERING SURFACE"]
PHY[physics-informed]
GO[GridOS]
DER[DERIM]
NB[NeuralBridge]
ROB[robot-lidar-fusion]
end
subgraph SYSTEM["SYSTEM LEVEL"]
TWIN[Digital Twin]
AI[Physics-Informed AI]
AGENTS[Agentic Systems]
SEC[Cybersecurity]
OBS[Observability]
end
CIM --> PHY
IEC --> GO
LF --> DER
GRIDAPPS --> GO
OPENEMS --> DER
PYPSA --> PHY
PP --> PHY
ODSS --> PHY
HELICS --> PHY
PHY --> TWIN
GO --> TWIN
DER --> TWIN
NB --> AGENTS
ROB --> TWIN
TWIN --> AI
AI --> AGENTS
AGENTS --> SEC
SEC --> OBS
Integration rather than reinvention.
That distinction matters in infrastructure.
Energy & Industrial
IEC 61850 · IEC 61968 · IEC 61970 · IEC 62351 · IEC 62443 · DNP3 · Modbus · OPC UA · MQTT · Sparkplug B · CIM
Safety & Systems Engineering
EN 50126 · EN 50128 · EN 50129 · RAMS · Functional safety concepts · Hardware-in-the-loop · Software-in-the-loop · Model-based engineering · Formal methods · Runtime assurance
Cybersecurity & Regulation
NERC CIP · NIS2 · EU Cyber Resilience Act · MITRE ATT&CK for ICS · Zero-trust architecture · Secure software supply chains · SBOM · Policy-as-code
Distributed & Simulation Systems
Kubernetes · Docker · Terraform · Kafka · NATS · RabbitMQ · HELICS · OMNeT++
| Layer | Technologies |
|---|---|
| Programming | Python · C · C++ · Rust · Go · Java · C# · TypeScript · JavaScript |
| Scientific / AI | PyTorch · TensorFlow · JAX · ONNX · NumPy · SciPy · Pandas · scikit-learn · OpenCV |
| Web / APIs | React · Next.js · Node.js · FastAPI · Flask · Django · WebSockets · REST |
| Data | PostgreSQL · TimescaleDB · InfluxDB · MongoDB · Redis · Neo4j · Kafka · NATS · RabbitMQ |
| Infrastructure | Linux · Docker · Kubernetes · Terraform · Ansible · AWS · Azure · GCP |
| Observability | Prometheus · Grafana · OpenTelemetry · Elasticsearch |
| Engineering / Simulation | HELICS · OMNeT++ · RTDS · OPAL-RT · MATLAB/Simulink · ROS 2 |
| Visualization | Three.js · WebGPU · WebGL · Unity · Unreal Engine · Blender · Plotly · Dash |
flowchart LR
A[Physical Infrastructure]
B[Electrical Engineering]
C[Industrial Protocols]
D[Telemetry]
E[Semantic Infrastructure]
F[Digital Twin]
G[Simulation]
H[Scientific AI]
I[Optimization]
J[Agentic Systems]
K[Cybersecurity]
L[Runtime Assurance]
M[Human / Operator Interface]
N[Automation / Robotics]
A --> B --> C --> D --> E --> F
F --> G
F --> H
G --> H --> I --> J --> K --> L
L --> M
L --> N
M --> A
N --> A
PyPSA · pandapower · OpenDSS · GridAPPS-D · HELICS · Grid2Op · Dynawo · Power Grid Model · PowSyBl
LF Energy · SOGNO · OpenEMS · OpenFMB-related architectures · CIM · IEC 61850 ecosystems
Grid foundation models · Physics-informed ML · Scientific ML · Graph-based grid intelligence · Reinforcement-learning environments · Agentic decision support
Digital twins · Runtime assurance · OT cybersecurity · Functional safety · HIL · SIL · Edge computing
flowchart LR
ASSET[Physical Asset] --> ENG[Engineering Model] --> DATA[Telemetry / Data] --> SEM[Semantic Model] --> TWIN[Digital Twin] --> PRED[Simulation / Prediction] --> AI[AI / Optimization] --> ASSURE[Assurance / Policy] --> ACTION[Control / Action]
A physical asset progressively becomes richer in digital representation.
That is the digital thread.
Not simply another:
AI application · dashboard · digital-twin visualization · grid simulator · automation framework · robotics repository
The larger direction is the integration of all of them.
flowchart LR
PHYSICS[Physics] --> ELEC[Electrical System] --> DIGITAL[Digital Representation] --> TWIN[Digital Twin]
TWIN --> SIM[Simulation]
TWIN --> DATA[Data]
SIM --> AI[AI / ML / RL]
DATA --> AI
AI --> POLICY[Policy + Assurance] --> ACTION[Controlled Action] --> INFRA[Physical Infrastructure] --> PHYSICS
flowchart LR
PHYSICS[PHYSICS] --> CORE
ELEC[ELECTRICAL ENGINEERING] --> CORE
DATA[DATA] --> CORE
SOFTWARE[SOFTWARE] --> CORE
AI[AI] --> CORE
SECURITY[CYBERSECURITY] --> CORE
AUTOMATION[AUTOMATION] --> CORE
HUMAN[HUMAN ENGINEERING] --> CORE
CORE["INTELLIGENT<br/>CYBER-PHYSICAL<br/>INFRASTRUCTURE"]
The objective is not to remove the engineering discipline beneath the software.
It is to make that discipline computable.
flowchart LR
PHYSICS[Physics] --> TWIN[Digital Twins] --> AI[AI / ML]
TWIN --> SIM[Simulation]
AI --> AGENTS[Agentic Systems]
SIM --> AGENTS
AGENTS --> ASSURE[Runtime Assurance]
ASSURE --> SEC[Cybersecurity]
SEC --> INFRA[Physical Infrastructure]
INFRA --> PHYSICS
The resulting class of systems is different:
Software that understands the physical systems it operates around.
This portfolio tracks the evolution from:
flowchart LR
RULES[Rules] --> MODELS[Physics Models] --> ML[Machine Learning] --> FM[Foundation Models] --> AGENTS[Agentic Systems] --> ASSURED[Assured Cyber-Physical Intelligence]
Current industry and research signals include:
Grid foundation models DOE's GridFM 2.0 initiative is targeting dramatically higher planning and scenario-analysis throughput using AI.
Dynamic grid foundation models GE Vernova's active DynaGridFM project combines generative AI, time-series modelling and graph neural networks for proactive grid decision support.
Agentic AI for power/OT DOE and LLNL's Stormbreaker testbed is explicitly focused on evaluating LLMs and agentic AI in power-system and OT environments.
Open-source AI operations LF Energy's AINETUS integrates AI decision support with Grid2Op, OperatorFabric and SOGNO; OpenGridFM is progressing through LF Energy's project lifecycle.
Digital-substation evolution IEC's 2026 IEC 61850 series release demonstrates that the interoperability layer itself continues to evolve.
Critical-infrastructure AI governance NIST is developing a dedicated Trustworthy AI in Critical Infrastructure profile covering AI/IT/OT/ICS intersections and operational properties such as determinism, explainability and graceful degradation.
flowchart LR
WEB[GRIMALDI.CA<br/>Strategic / Visual]
GITHUB[GITHUB<br/>Engineering]
RESEARCH[Research]
SOFTWARE[Software]
SYSTEMS[Systems]
ENERGY[Energy]
INTELLIGENCE[AI]
ROBOTICS[Robotics]
INFRA[Intelligent Infrastructure]
WEB --> GITHUB
GITHUB --> RESEARCH
GITHUB --> SOFTWARE
GITHUB --> SYSTEMS
RESEARCH --> ENERGY
SOFTWARE --> INTELLIGENCE
SYSTEMS --> ROBOTICS
ENERGY --> INFRA
INTELLIGENCE --> INFRA
ROBOTICS --> INFRA
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The future of infrastructure will not be defined by AI alone.
It will be defined by the integration of:
Physics + Electrical Engineering + Software + Data + AI + Cybersecurity + Automation + Human Engineering
The systems worth building are those that can operate across these domains without losing the properties that make physical infrastructure trustworthy:
determinism · safety · resilience · observability · interoperability · explainability · accountability
VINCENZO GRIMALDI
Electrical Engineering · Cyber-Physical Systems · Critical Infrastructure · Digital Twins · Physics-Informed AI · Intelligent Engineering
grimaldi.ca
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Vincenzo.grimaldi.engineering@gmail.com