Expensive models and experiments
PIML can reduce repeated simulation or experimental cost while retaining the governing knowledge used in Electrical and Electronics Engineering.
Engineering Research Community
Electrical and Electronics Engineering & Physics-Informed Machine Learning
Combining circuit laws, electromagnetic principles, dynamics and operational data
Electrical and Electronics Engineering (EEE) is one of the engineering disciplines most naturally suited to Physics-Informed Machine Learning (PIML). Electrical systems are governed by well-established mathematical and physical principles—from Kirchhoff’s laws and Maxwell’s equations to machine dynamics, circuit equations, electromagnetic field equations, power-flow equations, converter dynamics, control laws, and electrochemical models for energy-storage devices.
At the same time, modern electrical systems are becoming increasingly complex. Renewable-energy integration, smart grids, electric vehicles, battery storage, power-electronic converters, distributed generation, microgrids, intelligent electrical machines, IoT-enabled monitoring, and digital twins generate enormous quantities of data and require increasingly sophisticated modelling and control.
This page presents ten focused research areas, degree-level project pathways, selected publications and direct support through the PIMLS biweekly members meeting.
This Electrical and Electronics Engineering guide covers Physics-Informed Neural Networks (PINNs), physics-guided machine learning, scientific machine learning, neural operators, hybrid models and engineering digital twins. Explore the research and project pathways below, then join the Physics-Informed Machine Learning Society to connect with the international PIMLS community.
The central ideaCircuit and field laws + measurements + constrained learning and control
10focused research areas
3academic project pathways
6selected publications
Biweeklymember research meeting
Why this combination matters
Why Electrical and Electronics Engineering Needs Physics-Informed Learning
Use available scientific knowledge to make limited data more useful, transparent and testable.
Incomplete engineering models
Learn uncertain parameters, closures or discrepancies around an inspectable mechanistic foundation.
Transfer across conditions
Test whether structured models generalize across geometries, materials, assets, operating regimes or sites.
Trustworthy evidence
Use physical residuals, independent measurements, uncertainty and conventional engineering baselines before deployment.
Ten focused directions
Major Electrical and Electronics Engineering PIML Research Areas
Each card connects a meaningful Electrical and Electronics Engineering question with suitable scientific knowledge, modelling choices and evidence needed to test it.
01
Power Systems
Power systems represent one of the clearest opportunities for Physics-Informed Machine Learning. Modern grids contain: Conventional generation + Renewable generation + Storage + Power electronics + EV charging + Distributed generation + Microgrids + Smart loads This produces increasingly nonlinear and…
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
02
Smart Grids
The modern electrical grid is becoming a cyber-physical system. Millions of measurements can originate from: Smart meters • PMUs • substations • renewable plants • EV chargers • batteries • IoT devices Purely data-driven AI can analyze this information, but physical grid constraints remain important. PIML…
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
03
Renewable Energy Systems
Renewable energy presents another major research opportunity. Potential applications include:
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
04
Electrical Machines
Electrical machines involve a combination of: Electromagnetics + Electrical Circuits + Mechanical Dynamics + Thermal Behaviour This makes them particularly attractive for multiphysics PIML. Potential research areas include: The 2026 review on physics-informed AI for electrical systems specifically discusses…
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
05
Power Electronics
Power electronics is another highly promising area. Converters are governed by known circuit equations but can exhibit nonlinear, switched and dynamic behaviour. PIML can therefore be investigated for: This is already supported by published research. Researchers have demonstrated PIML for parameter…
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
06
Electric Vehicles
Electric vehicles combine several areas of EEE: Battery + Motor + Power Electronics + Control + Thermal Management + Charging + Grid Interaction Consequently, EVs provide numerous interdisciplinary PIML research opportunities. Potential topics include: ---
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
07
Battery and Energy-Storage Systems
Battery systems are particularly suitable for physics-informed learning because they combine measurable operational data with known physical and electrochemical behaviour. Researchers may investigate: State of Charge (SOC) State of Health (SOH) Remaining Useful Life (RUL) Capacity Fade Thermal Behaviour…
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
08
Electromagnetics
Electromagnetic systems are fundamentally governed by Maxwell's equations. This makes electromagnetics conceptually one of the strongest candidates for physics-informed learning. Potential applications include: Traditional electromagnetic simulation may require computationally intensive numerical techniques.…
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
09
Control Systems
Control engineering already combines mathematics, physical models and real-time data. Potential PIML research areas include: A particularly interesting research direction is combining: Physics-Informed Learning + Reinforcement Learning + Control Theory The objective is to develop intelligent controllers that…
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
10
Fault Detection and Predictive Maintenance
Modern electrical infrastructure contains enormous numbers of assets: Transformers Motors Generators Converters Switchgear Cables Circuit breakers Batteries Inverters Renewable-energy systems AI is already used extensively for fault detection. PIML adds another dimension: rather than identifying faults…
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
Choose the right research depth
Projects for Every Academic Stage
Start with a scope that matches your time, mathematical background, experimental access and expected research contribution.
Project pathway 1
B.E./B.Tech
Learn the foundations with a bounded, measurable system.
- Introductory Power Systems model with a clear baseline and validation dataset
- Introductory Smart Grids model with a clear baseline and validation dataset
- Introductory Renewable Energy Systems model with a clear baseline and validation dataset
- Introductory Electrical Machines model with a clear baseline and validation dataset
- Introductory Power Electronics model with a clear baseline and validation dataset
- Introductory Electric Vehicles model with a clear baseline and validation dataset
Expected outcome
A reproducible implementation, clear baselines, a manageable dataset and physically meaningful validation.
Project pathway 2
M.Tech
Combine an engineering model, substantial data and rigorous comparison.
- Hybrid or inverse Renewable Energy Systems study across unseen operating conditions
- Hybrid or inverse Electrical Machines study across unseen operating conditions
- Hybrid or inverse Power Electronics study across unseen operating conditions
- Hybrid or inverse Electric Vehicles study across unseen operating conditions
- Hybrid or inverse Battery and Energy-Storage Systems study across unseen operating conditions
- Hybrid or inverse Electromagnetics study across unseen operating conditions
Expected outcome
A thesis-quality study with held-out regimes, mechanistic and data-only baselines, ablation and uncertainty.
Project pathway 3
Ph.D.
Address a publishable methodological, multiscale or deployment research gap.
- RLC circuits • DC motors • DC-DC converters • PV systems • battery equivalent circuits • simple microgrids
- New PINN architectures
- Graph-based PIML
- Neural operators
- Multi-physics learning
- Bayesian PIML
Expected outcome
New methodology or validated engineering insight, multi-regime evidence, reproducible software and journal publications.
From idea to evidence
A Strong PIML Project Workflow
01
Define
Choose one Electrical and Electronics Engineering question and a measurable engineering output.
02
Model
State the governing relationships, constraints or validated domain knowledge you will retain.
03
Compare
Build mechanistic and data-only baselines before the hybrid model.
04
Validate
Hold out experiments, conditions, assets, sites or regimes at the deployment level.
05
Publish
Report uncertainty, ablation, limitations, data lineage and reproducible code.
Read before you modelDiscuss Your Literature
Selected Publications and Why They Matter
Use this focused reading list to understand the general PIML framework, direct Electrical and Electronics Engineering evidence and suitable hybrid modelling methods.
Literature review advice
Do not list papers only. Compare the engineering question, incorporated knowledge, data, split strategy, baselines, uncertainty and evidence level.
This source is included in the Electrical and Electronics Engineering literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Electrical and Electronics Engineering.
Read publication or record This source is included in the Electrical and Electronics Engineering literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Electrical and Electronics Engineering.
Read publication or record This source is included in the Electrical and Electronics Engineering literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Electrical and Electronics Engineering.
Read publication or record This source is included in the Electrical and Electronics Engineering literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Electrical and Electronics Engineering.
Read publication or record This source is included in the Electrical and Electronics Engineering literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Electrical and Electronics Engineering.
Read publication or record This source is included in the Electrical and Electronics Engineering literature guide because it demonstrates or reviews a relevant physics-informed, hybrid, inverse, surrogate or scientific-machine-learning approach. Read the methods, data split, baselines and validation evidence—not only the reported accuracy.
How to use this paper: Use this paper to refine the research question, identify a defensible physical prior and compare evidence requirements for Electrical and Electronics Engineering.
Read publication or record Build an interdisciplinary team
Where Electrical and Electronics Engineering Can Collaborate
Computer Science
Algorithms, optimization and trustworthy AI.
Mechanical Engineering
Machines, robotics, thermal and electromechanical systems.
Chemical & Materials
Batteries, fuel cells and functional materials.
Civil & Infrastructure
Smart buildings, grids and infrastructure sensing.
Before you begin
Frequently Asked Research Questions
These answers help students avoid common scope, terminology and validation mistakes.
Still have a question?
Use the biweekly meeting form for research guidance.
Request accessNo. Electrical and Electronics Engineering projects may use physics-guided features, hybrid residual models, differentiable simulators, neural operators, constrained architectures or data assimilation. State exactly what knowledge is incorporated.
Choose one engineering question, a measurable output and a defensible mechanistic baseline. Add learning only where data can identify an uncertainty or discrepancy.
A meaningful question, justified prior knowledge, deployment-level holdouts, strong baselines, ablation, uncertainty, reproducibility and honest limitations.
Simulation can broaden coverage, but simulation-only evidence cannot establish real-system accuracy. Use calibrated experiments, field measurements or trusted independent references appropriate to the claim.
Submit the biweekly members meeting form to discuss your project level, branch, data, model, validation plan and possible collaborators.
Take the next step
Bring your Electrical and Electronics Engineering research idea to PIMLS
Join the biweekly members meeting for project guidance, collaboration and publication planning—or contact the Society directly.
Meeting participation is requested through the Google form. Complete it carefully so the Society can understand your research interest.