Expensive models and experiments
PIML can reduce repeated simulation or experimental cost while retaining the governing knowledge used in Electrical, Electronics and Power Engineering.
Engineering Research Community
Electrical, Electronics and Power Engineering & Physics-Informed Machine Learning
Physics-grounded modelling, learning and validation for Electrical, Electronics and Power Engineering
Electrical, Electronics and Power Engineering combines circuits and devices, instrumentation, machines, converters, power networks, protection and control. PIML can build cross-domain digital twins from component measurements to grid operation.
The broad curriculum creates an integration opportunity, but research must still define a precise system boundary and validate interfaces between electronic, thermal, mechanical and network models.
This page presents ten focused research areas, degree-level project pathways, selected publications and direct support through the PIMLS biweekly members meeting.
This Electrical, Electronics and Power 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 ideaEstablished Electrical, Electronics and Power Engineering knowledge + measurements and simulation + machine learning
10focused research areas
3academic project pathways
6selected publications
Biweeklymember research meeting
Why this combination matters
Why Electrical, Electronics and Power 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, Electronics and Power Engineering PIML Research Areas
Each card connects a meaningful Electrical, Electronics and Power Engineering question with suitable scientific knowledge, modelling choices and evidence needed to test it.
01
Electronic Measurement Interfaces
Signal chains transform physical quantities. PIML opportunities: Model calibration, bandwidth, noise and drift.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
02
Power Semiconductor Systems
Device losses shape converter efficiency and life. PIML opportunities: Use compact electrothermal hybrids with bench evidence.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
03
Converters and Drives
Switching links electronics to machines. PIML opportunities: Build multirate twins with hardware-in-loop tests.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
04
Electrical Machines
Flux, torque, heat and vibration interact. PIML opportunities: Estimate state and degradation across duty cycles.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
05
Power Networks
Topology and AC laws constrain system state. PIML opportunities: Use feasible estimation and operator-aware optimization.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
06
Renewable Energy Systems
Resources connect through electronic interfaces. PIML opportunities: Co-model source physics, conversion and grid services.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
07
Storage Systems
Batteries and converters have coupled limits. PIML opportunities: Use electrothermal health models in dispatch.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
08
Instrumentation and Protection
Measurement quality governs action. PIML opportunities: Maintain traceability and deterministic protective layers.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
09
Power Quality and EMC
Electronic switching creates network disturbances. PIML opportunities: Use circuit/field evidence for diagnosis and mitigation.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
10
Microgrid Control
Devices coordinate under grid and island modes. PIML opportunities: Use safe distributed control with communication tests.
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.
- AC-feasible load-flow learner
- PMU state estimator
- frequency-response parameter estimator
- topology-aware fault classifier
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.
- dynamic-security neural operator
- inverter-rich grid hybrid twin
- robust microgrid MPC
- cyber-physical grid testbed
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.
- certifiable learned optimal power flow
- foundation models for grid dynamics
- adaptive protection with formal safeguards
- federated privacy-preserving utility 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, Electronics and Power 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, Electronics and Power 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, Electronics and Power 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, Electronics and Power Engineering.
Read publication or record This source is included in the Electrical, Electronics and Power 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, Electronics and Power Engineering.
Read publication or record This source is included in the Electrical, Electronics and Power 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, Electronics and Power Engineering.
Read publication or record This source is included in the Electrical, Electronics and Power 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, Electronics and Power Engineering.
Read publication or record This source is included in the Electrical, Electronics and Power 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, Electronics and Power Engineering.
Read publication or record This source is included in the Electrical, Electronics and Power 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, Electronics and Power Engineering.
Read publication or record Build an interdisciplinary team
Where Electrical, Electronics and Power Engineering Can Collaborate
Computer Science
Scientific ML, optimization, trustworthy AI and reproducible research software.
Applied Mathematics
Differential equations, numerical methods, inverse problems and uncertainty.
Sensing & Control
Instrumentation, data acquisition, state estimation and responsible deployment.
Domain Laboratories
Experiments, calibration, validation evidence and practical expertise for Electrical, Electronics and Power Engineering.
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, Electronics and Power 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, Electronics and Power 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.