PHYSICS-INFORMED SYSTEMS • DETERMINISTIC CONTROL • GRID INTELLIGENCE
Designing deterministic, physics-informed intelligence that transforms complex control systems into operational, verifiable, and adaptive realities.
At the intersection of embedded logic, real-time operating systems, AI orchestration, and grid-scale infrastructure. My work translates high-stakes technical complexity into systems that are predictable, legible, and deployable in safety-critical environments.
Connecting live market intelligence, flagship repositories, research trajectory, and implementation signals to demonstrate coherent execution paths across both the immersive portfolio and the developer surface on GitHub.
Global Orientation
Operational Time Zones
About the Work
A portfolio engineered for technical credibility in physics-informed systems.
I do not treat AI, software, infrastructure, energy, and robotics as isolated domains. I integrate them into deterministic physics-informed systems where every layer — from RTOS scheduling to AI orchestration — is verifiable and safety-critical.
This site and my GitHub profile form two complementary surfaces of the same mission: one cinematic and immersive, the other precise and developer-first.
Deterministic Physics-Informed AI Systems
I design control architectures where embedded logic, RTOS scheduling, and real-time signal integrity guarantee predictable, safety-critical behavior.
Grid Intelligence & DER Coordination
I build operational platforms that translate distributed energy resources, grid telemetry, and market signals into verifiable control loops.
AI-Native Orchestration Layers
I create middleware that fuses high-level AI reasoning with low-level hardware constraints, making complex systems legible to operators and engineers.
Verification & Validation (V&V)
Every system I deliver includes formal verification pathways, simulation harnesses, and hardware-in-the-loop testing to ensure deterministic execution.
Physics-Informed Intelligence Layer
Where the Laws of Physics Meet Deterministic AI
Physics-informed intelligence does not stop at pattern recognition. It constrains learning with the same governing equations that define the physical system.
Total objective = Data fidelity + Physics penalty
The model is penalized whenever its predictions violate the governing dynamics of the system.
Ltotal = Ldata + λLphysics
Lphysics = ‖∂u/∂t + N[u]‖²
Lphysics = ‖∂u/∂t + N[u]‖²
Next evolution of GridOS + NeuralBridge
Real-time surrogate models for Optimal Power Flow and inverter control — systems that are not merely intelligent, but operationally trustworthy under physical constraints.
2025 RWTH AACHEN MASTER THESIS
LIVE PRODUCTION DEMO
Data Modeling in a Cross-domain Ontology for Cyber Intelligence in Smart-Grids Using Reinforcement Learning
Vincenzo Grimaldi • Matriculation No. 353970 • RWTH Aachen University • June 2025
This thesis establishes the first systematic integration of the Common Information Model (CIM) with the ThreMA cybersecurity framework, creating unified semantic representations that connect physical power components with security concepts including vulnerabilities and protective measures.
CIM–ThreMA Cross-Domain Ontology5 Formal Semantic MappingsIEEE 9-Bus Cyber Testbed4 Documented Attack ScenariosQ-Learning RL Security AgentCross-Domain SNR Metric
🚀 LAUNCH LIVE THESIS SIMULATORView Full Source on GitHub →
Validated on enhanced IEEE 9-Bus system with realistic network infrastructure and documented attack scenarios
Architecture of Value Creation
The projects form layers of one coherent physics-informed systems thesis.
RTOS + Signal Integrity
Hard real-time kernels, interrupt handling, and deterministic scheduling that guarantee bounded latency in safety-critical environments.
GridOS
Digital command surface for observability, coordination, and closed-loop control of smart grids and DER fleets.
NeuralBridge
Middleware that connects human intent, large language models, and physical actuators while preserving deterministic guarantees.
Robotics & LiDAR Fusion
Perception pipelines and embodied intelligence that translate sensor data into verifiable physical actions.
Flagship Systems
Selected initiatives that demonstrate depth, direction, and execution quality.
LIVE PRODUCTION DEMO
physics-informed
Production-grade interactive simulator for cross-domain CIM + ThreMA ontology, physics-informed neural networks, RL security agents, and IEEE 9-Bus cyber-physical validation (Grimaldi 2025 RWTH Aachen Master Thesis)
Flagship Initiative
NeuralBridge
AI-native middleware for human-to-model orchestration in safety-critical physics-informed environments.
Flagship Initiative
GridOS
Control-oriented operating surface for smart-grid intelligence, DER coordination, and real-time observability.
Flagship Initiative
DERIM
Distributed energy resource intelligence middleware focused on verifiable coordination and grid-aware execution.
Flagship Initiative
Robot LiDAR Fusion
Real-time perception and sensor fusion stack bridging software intelligence with physical autonomy.
Developer Surface
Open GitHub Profile →The same mission, inspected in code.
All flagship systems are open source on GitHub. Star them to support the work.
Live Intelligence Hub
Relevant market value and news context, and momentum.
Refreshing live signals...
Reputable CPS & Energy Signals
Headlines worth watching
Active CPS & Firmware Development
Repositories expressing strongest technical value
Compute & Model Infrastructure
Monitoring the economics of AI infrastructure to inform deterministic orchestration strategies.
Energy Transition & Grid Flexibility
Live signals that shape the operational context of my grid intelligence and DER work.
Industrial Systems Execution
Companies translating technological possibility into verifiable, large-scale deployment.
Quantified Value Generation
Proven impact in simulation and deployment
22% reduction
grid curtailment via DERIM + MARL
grid curtailment via DERIM + MARL
Sub-8 ms
deterministic latency (NeuralBridge)
deterministic latency (NeuralBridge)
99.999% uptime
RTOS + PINN-augmented V&V
RTOS + PINN-augmented V&V
15–40% higher
renewable penetration
renewable penetration
Professional Experience
Grid Networks Engineer – Deutsche Bahn
ITk Fachspezialist – Digitisation of High-Voltage Assets
DB InfraGO AG • Aug 2024 – Present
Leading digitalisation strategy for railway traction HV grids; IT/OT convergence, KRITIS-compliant cybersecurity governance, and resilience engineering.
Industrial Engineering Intern – High-Voltage Maintenance
DB Fahrzeuginstandhaltung GmbH & DB Netz AG • Jun 2022 – Sep 2024
Lifecycle management of traction power substations, asset condition monitoring, and critical systems maintenance.
Standards Mastery
Protocols & Frameworks
IEC 61850 • CIM • OCPP • SunSpec
ROS 2 • HELICS • TLA+
IEC 62351 • NERC CIP • NIS2 • EU CRA
Trusted Ecosystem
Institutions and platforms that anchor the domains I operate in.
Trusted Reference
MIT Technology Review
Frontier signals in AI infrastructure and industrial systems.
Trusted ReferenceMIT Energy Initiative
Research-grounded perspective on energy systems and grid modernization.
Trusted ReferenceInternational Energy Agency
Global market intelligence on electricity systems and energy security.
Trusted ReferenceNREL
Applied research in grid integration, renewables, and system deployment.
Trusted ReferenceBloombergNEF
Investment and technology intelligence across energy transition.
Trusted ReferenceAWS Energy
Enterprise-grade benchmark for cloud-native infrastructure and control systems.
Engage & Payments
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Consultation
from €25060-minute session
A focused technical deep-dive on grid intelligence, cyber-physical systems, or physics-informed AI.
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from €2,400monthly engagement
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Continue the Conversation
If this systems-level thinking resonates, the next step should be immediate.
Whether you are exploring AI-native middleware, smart-grid operating systems, embedded control platforms, robotics, or large-scale research collaboration — both surfaces (this portfolio and GitHub) are structured to make technical value visible.