Expensive models and experiments
PIML can reduce repeated simulation or experimental cost while retaining the governing knowledge used in Electronics and Electrical Engineering.
Engineering Research Community
Electronics and Electrical Engineering & Physics-Informed Machine Learning
Physics-grounded modelling, learning and validation for Electronics and Electrical Engineering
Electronics and Electrical Engineering integrates electronic circuits and instrumentation with machines, power conversion, electrical networks and control. PIML can bridge component nonidealities and system operation across electrical and electronic scales.
Compared with the similarly worded Electrical, Electronics and Power branch, this page treats electronics–electrical integration broadly and does not assume a dedicated utility-power specialization. Embedded devices, machines, controls and energy systems all remain central.
This page presents ten focused research areas, degree-level project pathways, selected publications and direct support through the PIMLS biweekly members meeting.
This Electronics and Electrical 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 Electronics and Electrical Engineering knowledge + measurements and simulation + machine learning
10focused research areas
3academic project pathways
6selected publications
Biweeklymember research meeting
Why this combination matters
Why Electronics and Electrical 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 Electronics and Electrical Engineering PIML Research Areas
Each card connects a meaningful Electronics and Electrical Engineering question with suitable scientific knowledge, modelling choices and evidence needed to test it.
01
Electronic Circuits
Devices and parasitics affect signal/control paths. PIML opportunities: Learn compact residual models with board evidence.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
02
Instrumentation
Sensors determine knowledge of electrical state. PIML opportunities: Model calibration, bandwidth, drift and uncertainty.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
03
Power Electronics
Converters connect electronics to energy. PIML opportunities: Build electrothermal twins with switching tests.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
04
Electric 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
Drives and Motion Systems
Converters and machines operate in feedback. PIML opportunities: Use hybrid models inside safeguarded control.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
06
Energy Storage Interfaces
Batteries and converters share constraints. PIML opportunities: Co-model electrothermal health and power delivery.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
07
Renewable Energy Electronics
Sources require electronic conversion. PIML opportunities: Fuse source physics, MPPT/control and equipment state.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
08
Electrical Networks
Circuit laws constrain flows and voltages. PIML opportunities: Use feasible estimators and topology-aware surrogates.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
09
Embedded Electrical Control
Real-time computation affects physical performance. PIML opportunities: Validate latency, precision and fallback.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
10
Power Quality and EMC
Switching produces harmonics and coupling. PIML opportunities: Use circuit/field evidence for diagnosis.
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 Electronics and Electrical 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 Electronics and Electrical 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 Electronics and Electrical 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 Electronics and Electrical Engineering.
Read publication or record This source is included in the Electronics and Electrical 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 Electronics and Electrical Engineering.
Read publication or record This source is included in the Electronics and Electrical 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 Electronics and Electrical Engineering.
Read publication or record This source is included in the Electronics and Electrical 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 Electronics and Electrical Engineering.
Read publication or record This source is included in the Electronics and Electrical 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 Electronics and Electrical Engineering.
Read publication or record This source is included in the Electronics and Electrical 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 Electronics and Electrical Engineering.
Read publication or record Build an interdisciplinary team
Where Electronics and Electrical 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 Electronics and Electrical 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. Electronics and Electrical 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 Electronics and Electrical 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.