Expensive models and experiments
PIML can reduce repeated simulation or experimental cost while retaining the governing knowledge used in Electrical Engineering (Electronics and Power).
Engineering Research Community
Electrical Engineering (Electronics and Power) & Physics-Informed Machine Learning
Physics-grounded modelling, learning and validation for Electrical Engineering (Electronics and Power)
Electrical Engineering in Electronics and Power joins electronic devices, circuits, power electronics, machines, power systems and control. Its distinctive PIML opportunity is to connect semiconductor and converter behaviour to equipment and grid-level performance.
A strong model keeps scales explicit: switching devices determine converter losses and thermal stress; converters shape machine or grid dynamics; supervisory controls act under network and safety constraints.
This page presents ten focused research areas, degree-level project pathways, selected publications and direct support through the PIMLS biweekly members meeting.
This Electrical Engineering (Electronics and Power) 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 Engineering (Electronics and Power) knowledge + measurements and simulation + machine learning
10focused research areas
3academic project pathways
6selected publications
Biweeklymember research meeting
Why this combination matters
Why Electrical Engineering (Electronics and Power) 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 Engineering (Electronics and Power) PIML Research Areas
Each card connects a meaningful Electrical Engineering (Electronics and Power) question with suitable scientific knowledge, modelling choices and evidence needed to test it.
01
Semiconductor Power Devices
Conduction, switching and temperature govern losses. PIML opportunities: Learn compact residual models across bias and thermal regimes.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
02
DC–DC Converters
Parasitics and mode changes challenge averaged models. PIML opportunities: Build hybrid twins with switching-waveform validation.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
03
Grid-Tied Inverters
Control interacts with filters and grid impedance. PIML opportunities: Use physics-aware estimators under weak-grid conditions.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
04
Motor Drives
Converter and machine dynamics are coupled. PIML opportunities: Model torque, loss and temperature across speed/load.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
05
Power Modules and Packaging
Heat and stress drive degradation. PIML opportunities: Combine electrothermal models with accelerated-life data.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
06
Renewable Interfaces
PV and wind connect through converters. PIML opportunities: Co-model source, conversion and grid constraints.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
07
Battery Power Conversion
Storage state affects converter operation. PIML opportunities: Use electrothermal observers and safe charge/discharge limits.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
08
Microgrids
Multiple electronic sources coordinate locally. PIML opportunities: Develop stable hybrid control for grid/island transitions.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
09
Power Quality
Switching and loads create harmonics. PIML opportunities: Use circuit constraints for source localization and mitigation.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
10
Protection of Converter Systems
Fault currents differ from rotating machines. PIML opportunities: Combine transient models with deterministic trip logic.
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 Engineering (Electronics and Power) 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 Engineering (Electronics and Power) 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 Engineering (Electronics and Power) 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 Engineering (Electronics and Power).
Read publication or record This source is included in the Electrical Engineering (Electronics and Power) 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 Engineering (Electronics and Power).
Read publication or record This source is included in the Electrical Engineering (Electronics and Power) 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 Engineering (Electronics and Power).
Read publication or record This source is included in the Electrical Engineering (Electronics and Power) 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 Engineering (Electronics and Power).
Read publication or record This source is included in the Electrical Engineering (Electronics and Power) 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 Engineering (Electronics and Power).
Read publication or record This source is included in the Electrical Engineering (Electronics and Power) 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 Engineering (Electronics and Power).
Read publication or record Build an interdisciplinary team
Where Electrical Engineering (Electronics and Power) 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 Engineering (Electronics and Power).
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 Engineering (Electronics and Power) 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 Engineering (Electronics and Power) 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.