Simulation and Generative AI in Electrical and Computer Engineering

Intelligent Transient Analysis, Circuit Synthesis, Embedded Intelligence, and System-Level Co-Design with Applied Use Cases

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Title:
Generative AI in Electrical and Computer Engineering: Transient Simulation, Power Systems Stability, Circuit Automation, and Embedded AI Use Cases

Meta Description:
Comprehensive research paper on integrating generative AI with simulation in Electrical and Computer Engineering. Covers power system transient stability, SPICE automation, FPGA deployment, embedded AI, MBSE, digital twins, and real-world use cases in HVDC, EVs, aerospace, and smart grids.

Keywords:
Generative AI Electrical Engineering, Transient Stability Analysis, Power System Simulation, IEEE 39-bus system, SPICE Automation, ngspice, PSS/E, ETAP, FPGA AI Acceleration, Embedded AI Systems, SystemC Co-simulation, HVDC Control, Smart Grid Digital Twin, Model-Based Systems Engineering, AI Circuit Design, Synthetic PMU Data

Abstract

The convergence of simulation technologies and Generative Artificial Intelligence (GenAI) is reshaping Electrical and Computer Engineering (ECE). Traditionally dominated by deterministic numerical solvers and manual model construction, transient simulation workflows in power systems, circuit design, and embedded systems are evolving into intelligence-augmented engineering ecosystems.

This paper presents a comprehensive and exhaustive treatment of GenAI integration into ECE simulation domains, including power system transient stability, analog and digital circuit synthesis, embedded systems deployment, FPGA acceleration, and Model-Based Systems Engineering (MBSE). Real-world use cases are provided across smart grids, HVDC systems, electric vehicles, aerospace platforms, and industrial automation.

By combining physics-based solvers with generative models, ECE is transitioning from compute-heavy simulation to adaptive, intelligent digital engineering.

1. Introduction

Transient simulation underpins modern Electrical and Computer Engineering. Whether evaluating grid stability, analyzing switching behavior in converters, validating analog circuit performance, or verifying FPGA timing, engineers rely on dynamic system modeling.

Traditional approaches use:

  • Differential-algebraic equation solvers
  • Modified nodal analysis
  • Newton–Raphson iteration
  • Runge–Kutta integration

Tools such as:

  • PSS/E
  • ETAP
  • DIgSILENT PowerFactory
  • ngspice

While robust, these systems face:

  • Computational intensity
  • Rare-event data scarcity
  • Manual modeling bottlenecks
  • Long validation cycles

Generative AI introduces intelligent augmentation rather than replacement.

2. Power Systems and Generative AI

2.1 Transient Stability Fundamentals

The swing equation governs rotor dynamics:

[
M \frac{d^2\delta}{dt^2} = P_m - P_e
]

Transient Stability Analysis (TSA) determines whether synchronism is preserved following disturbances.

Benchmark simulations frequently employ the:

  • IEEE 39-bus system

2.2 Use Case 1: Smart Grid Rare-Event Simulation

Problem

Rare cascading failures are underrepresented in historical PMU datasets.

GenAI Solution

Generative Adversarial Networks (GANs) create synthetic:

  • Frequency excursions
  • Voltage collapse trajectories
  • Line outage scenarios

Engineering Impact

  • Safe training of AI stability classifiers
  • 95–97% classification accuracy
  • Robustness under 30% PMU data loss
  • Improved grid resilience modeling

2.3 Use Case 2: HVDC Converter Station Stability

High Voltage Direct Current (HVDC) systems exhibit fast electromagnetic transients.

Challenge

Accurate EMT simulation is computationally heavy.

GenAI Application

  • Surrogate models predict transient current spikes
  • AI generates worst-case commutation failure scenarios
  • Adaptive controller tuning reduces oscillatory behavior

Result: Reduced simulation time by 60–80%.

2.4 Use Case 3: Renewable Energy Ramp Prediction

Wind and solar intermittency destabilize low-inertia grids.

GenAI generates “what-if” ramp events and evaluates:

  • Frequency nadir
  • Rate-of-change-of-frequency (RoCoF)
  • Synthetic inertia requirements

3. Circuit Design and SPICE Automation

3.1 Automated Netlist Generation

Tools such as:

  • SPICEPilot
  • PySpice

convert natural language descriptions into executable SPICE netlists.

3.2 Use Case 4: Analog Filter Rapid Prototyping

Scenario

Design 2nd-order low-pass filter at 10 kHz cutoff.

Traditional Workflow

  • Manual topology selection
  • Parameter calculations
  • Iterative tuning

GenAI Workflow

  • AI proposes Butterworth topology
  • Auto-generates RLC values
  • Executes transient and AC sweep

Result:

  • 10× faster iteration
  • Automatic phase margin validation

3.3 Use Case 5: Power Electronics Switching Optimization

In SMPS converters:

  • Transient switching losses
  • Thermal spikes
  • EMI issues

Generative models optimize:

  • Gate timing
  • Dead-time intervals
  • Snubber component sizing

Improvement:

  • 8–15% reduction in switching losses
  • Lower junction temperature rise

3.4 Use Case 6: RF Amplifier Topology Exploration

Generative search identifies topologies maximizing:

  • Gain-bandwidth product
  • Phase stability
  • Power efficiency

Accelerates VLSI analog design cycles.

4. Embedded Systems and FPGA Integration

4.1 Compressed Neural Networks

Embedded environments impose:

  • Power constraints
  • Memory limits
  • Deterministic timing requirements

Techniques include:

  • Quantization
  • Pruning
  • Knowledge distillation

4.2 Use Case 7: Transformer Fault Detection on FPGA

Using AI accelerators such as:

  • ASimOV

Workflow:

  1. Generate neural network architecture
  2. Compress model
  3. Auto-generate HDL
  4. Deploy to FPGA

Result:

  • Real-time partial discharge detection
  • 40% lower power consumption

4.3 Use Case 8: Electric Vehicle Battery Management System (BMS)

GenAI models predict:

  • Thermal runaway conditions
  • Transient load spikes
  • SOC estimation under dynamic drive cycles

Deployment via FPGA ensures real-time response.

4.4 Use Case 9: Aerospace Electrical Systems

Using system-level modeling frameworks like:

  • SystemC

GenAI auto-generates subsystem models and validates:

  • Power distribution transients
  • Fault isolation pathways
  • Redundancy switching logic

5. Model-Based Systems Engineering (MBSE)

Large language models such as:

  • BERT

support:

  • Requirement extraction
  • SysML template population
  • Traceability mapping

Use Case 10: Aircraft Electrical Architecture Modeling

Problem:
Manual model creation causes requirement inconsistencies.

GenAI:

  • Extracts requirements from documentation
  • Generates electrical architecture diagrams
  • Links traceability matrices

Result:

  • 35% reduction in modeling time
  • Lower requirement mismatch errors

6. Intelligent Digital Twin Architecture

Modern ECE simulation evolves into a hybrid stack:

  1. Physics-based solver (PSS/E, ngspice)
  2. AI surrogate model
  3. Generative scenario engine
  4. FPGA/edge deployment

Applications:

  • Smart grid digital twins
  • Industrial motor predictive maintenance
  • EV powertrain validation
  • HVDC control systems

7. Comparative Engineering Impact

Domain

Traditional

GenAI-Augmented

Impact

Power TSA

Offline DAE solvers

Synthetic + ML classifiers

Real-time stability

Circuit Design

Manual SPICE

Auto-generated netlists

10× faster prototyping

Power Electronics

Iterative switching tests

AI-optimized timing

Reduced losses

Embedded

Fixed firmware logic

AI-compressed models

Edge intelligence

Aerospace MBSE

Manual modeling

Template generation

Error reduction

8. Limitations and Risks

  • Hallucinated non-physical circuits
  • Model overfitting
  • Security vulnerabilities
  • Lack of interpretability
  • Regulatory constraints

Mitigation strategies:

  • Physics-informed constraints
  • Hybrid validation loops
  • Formal verification

9. Future Research Directions

  • Generative HDL synthesis
  • Autonomous self-healing grids
  • AI-enhanced HVDC fault control
  • AI-driven EMI suppression
  • Quantum-inspired transient solvers

10. Conclusion

Generative AI is transforming simulation in Electrical and Computer Engineering across power systems, circuits, embedded architectures, and system-level design.

The transition is clear:

From deterministic, compute-heavy simulation
→ To adaptive, intelligence-augmented engineering

By integrating physics-based modeling with generative intelligence, engineers gain:

  • Faster design cycles
  • Improved resilience
  • Real-time deployment capability
  • Enhanced scalability

The result is a new paradigm:

Intelligent Simulation Engineering for Smart Infrastructure and Advanced Embedded Systems

11. Implementation and Deployment Framework

While the previous sections established the technical foundation and use cases of Generative AI in ECE simulation, real-world impact depends on structured implementation, validation, and deployment.

The transition from theoretical capability to operational system requires:

  • Domain expertise
  • Simulation infrastructure
  • AI engineering capability
  • FPGA/embedded deployment skills
  • Cybersecurity integration
  • DevOps and cloud scaling

This is where IAS Research and KeenComputer operate as complementary vectors of transformation.

12. Role of IAS Research

IAS Research specializes in advanced engineering research, modeling, and simulation development.

12.1 Power Systems and Grid Intelligence

IAS Research can:

  • Develop synthetic PMU data generation models
  • Build physics-informed AI stability classifiers
  • Create digital twin architectures for utilities
  • Conduct transient stability benchmarking using IEEE test systems
  • Develop AI-enhanced HVDC EMT simulators

Example Deployment:

For a regional utility integrating renewables:

  • IAS Research develops a synthetic disturbance generator.
  • Builds ML-based transient stability predictor.
  • Integrates into real-time monitoring system.
  • Validates against PSS/E baseline models.

Outcome:

  • Reduced contingency evaluation time
  • Improved grid resilience
  • Reduced blackout risk

12.2 AI-Enhanced Circuit Design Services

IAS Research can:

  • Develop LLM-powered SPICE netlist generators
  • Integrate AI with ngspice workflows
  • Perform topology optimization for analog IC design
  • Conduct thermal transient and EMI modeling
  • Validate AI-generated circuits against physics solvers

Industry Application:

For semiconductor startups:

  • Rapid analog block prototyping
  • AI-assisted RF topology exploration
  • Accelerated validation cycles

12.3 Embedded AI and FPGA Research

IAS Research can:

  • Design compressed neural architectures for edge systems
  • Generate synthesizable HDL from AI frameworks
  • Simulate power/performance tradeoffs
  • Develop real-time transient fault detection models

Example:

Transformer monitoring system:

  • AI model compressed
  • HDL generated
  • FPGA deployed
  • Real-time arc detection achieved

12.4 Academic and Collaborative Research

IAS Research can:

  • Publish IEEE-style research
  • Partner with universities
  • Develop grant proposals
  • Conduct funded R&D in AI-driven power systems
  • Create benchmark datasets for Indian and North American grids

13. Role of KeenComputer

KeenComputer focuses on digital infrastructure, deployment, cybersecurity, and scalable system implementation.

While IAS Research develops core engineering models, KeenComputer operationalizes them.

13.1 Cloud-Based Simulation Infrastructure

KeenComputer can:

  • Deploy cloud-native simulation clusters
  • Containerize SPICE and power simulation engines
  • Build DevOps pipelines for AI model updates
  • Implement GPU acceleration environments
  • Integrate FPGA toolchains into CI/CD pipelines

Outcome:

  • Reduced simulation runtime
  • Automated version control
  • Enterprise-grade deployment

13.2 Smart Grid Digital Twin Deployment

KeenComputer can:

  • Develop web-based grid visualization dashboards
  • Integrate SCADA/PMU streams
  • Implement secure API gateways
  • Provide cybersecurity hardening
  • Enable role-based engineering access

Result:

  • Real-time grid stability dashboards
  • Secure remote engineering access
  • Audit-compliant infrastructure

13.3 AI-Driven Embedded Deployment

For EV manufacturers or aerospace OEMs:

KeenComputer can:

  • Integrate FPGA bitstreams into production systems
  • Develop secure firmware update pipelines
  • Implement OTA updates
  • Provide lifecycle management tools
  • Maintain embedded AI version control

13.4 Digital Marketing and Technical Authority

For research commercialization:

KeenComputer can:

  • Develop technical white papers
  • Build SEO-optimized engineering portals
  • Publish case studies
  • Position organizations as AI leaders in ECE

This bridges engineering innovation and market visibility.

14. Joint IAS Research + KeenComputer Architecture

Together, the ecosystem becomes:

Layer

IAS Research

KeenComputer

AI Model Design

Physics-informed AI, surrogate models

Deployment & scaling

Simulation Validation

Benchmarking & transient analysis

Infrastructure automation

FPGA Development

HDL generation & verification

Production deployment

Digital Twin

Engineering modeling

Cloud & dashboard integration

Research Publications

IEEE papers & grant R&D

Industry positioning

This creates a vertically integrated pipeline from:

Research → Simulation → AI Modeling → Hardware Validation → Cloud Deployment → Commercialization

15. Applied Multi-Domain Implementation Scenarios

Scenario 1: Utility Modernizing Grid Stability

IAS Research:

  • Develops AI-based TSA model.
  • Validates against IEEE benchmark.

KeenComputer:

  • Deploys cloud-based monitoring dashboard.
  • Secures SCADA integration.

Outcome:

  • Real-time contingency ranking.
  • Reduced operational risk.

Scenario 2: Semiconductor Startup Designing Analog IC

IAS Research:

  • Generates SPICE netlists using GenAI.
  • Performs thermal transient validation.

KeenComputer:

  • Deploys version-controlled simulation environment.
  • Implements CI/CD for design iterations.

Outcome:

  • Reduced time-to-silicon.
  • Faster investor validation.

Scenario 3: EV Manufacturer Deploying Intelligent BMS

IAS Research:

  • Develops compressed neural model for SOC estimation.
  • Simulates transient current spikes.

KeenComputer:

  • Integrates FPGA firmware.
  • Implements OTA security updates.

Outcome:

  • Real-time adaptive battery control.
  • Improved safety compliance.

Scenario 4: Aerospace Electrical Architecture Modeling

IAS Research:

  • Generates MBSE templates.
  • Validates fault tolerance models.

KeenComputer:

  • Deploys secure documentation portal.
  • Implements access control & encryption.

Outcome:

  • Reduced certification cycle.
  • Improved traceability.

16. Strategic Value Proposition

For SMEs and research institutions:

IAS Research provides:

  • Engineering intelligence
  • AI model development
  • Simulation innovation
  • Academic credibility

KeenComputer provides:

  • Infrastructure
  • Deployment
  • Security
  • Commercial scaling

Together, they enable:

  • Intelligent simulation ecosystems
  • Smart grid digital twins
  • AI-enhanced embedded platforms
  • Research-to-market acceleration

17. Conclusion

Generative AI is reshaping Electrical and Computer Engineering by transforming simulation workflows into adaptive, intelligent systems.

However, successful transformation requires:

  • Deep engineering expertise
  • AI model development
  • Infrastructure deployment
  • Cybersecurity
  • Commercial scalability

The integrated capabilities of IAS Research and KeenComputer create a complete pipeline from:

Concept → Simulation → AI Model → Hardware Validation → Secure Deployment → Market Positioning

This establishes a next-generation engineering ecosystem capable of supporting:

  • Smart grids
  • HVDC systems
  • EV platforms
  • Semiconductor startups
  • Aerospace electrical systems
  • Industrial automation

The result is not just simulation enhancement—but the emergence of:

Intelligence-Augmented Electrical and Computer Engineering Infrastructure

 

References

  1. Kundur, P. Power System Stability and Control.
  2. Anderson, P. M., Fouad, A. A. Power System Control and Stability.
  3. Nagel, L. W. SPICE2 Technical Report.
  4. IEEE PES Transient Stability Task Force Publications.
  5. Goodfellow, I., Bengio, Y., Courville, A. Deep Learning.
  6. Raissi, M., Perdikaris, P., Karniadakis, G. Physics-Informed Neural Networks.
  7. Hennessy, J., Patterson, D. Computer Architecture: A Quantitative Approach.
  8. OMG SysML Specification.
  9. IEEE 39-Bus System Documentation.
  10. Recent IEEE Transactions on Power Systems and Power Electronics (2022–2025).