Expensive models and experiments
PIML can reduce repeated simulation or experimental cost while retaining the governing knowledge used in Smart and Sustainable Energy.
Engineering Research Community
Smart and Sustainable Energy & Physics-Informed Machine Learning
Physics-grounded modelling, learning and validation for Smart and Sustainable Energy
Smart and Sustainable Energy integrates renewable resources, storage, efficient conversion, smart grids, buildings, transport, markets and lifecycle decarbonization. PIML can connect component physics and network dynamics with sensing, forecasting and constrained decisions.
A smart operational improvement is not automatically sustainable. Studies must state system and lifecycle boundaries, additionality and counterfactuals, embodied impacts, reliability and equity, while distinguishing measured emissions from modelled scenarios.
This page presents ten focused research areas, degree-level project pathways, selected publications and direct support through the PIMLS biweekly members meeting.
This Smart and Sustainable Energy 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 Smart and Sustainable Energy knowledge + measurements and simulation + machine learning
10focused research areas
3academic project pathways
6selected publications
Biweeklymember research meeting
Why this combination matters
Why Smart and Sustainable Energy 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 Smart and Sustainable Energy PIML Research Areas
Each card connects a meaningful Smart and Sustainable Energy question with suitable scientific knowledge, modelling choices and evidence needed to test it.
01
Solar Energy
Radiation, temperature and conversion shape output. PIML opportunities: Use physical device/system models with site holdouts.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
02
Wind Energy
Aerodynamics and structural loads interact. PIML opportunities: Build turbine/farm surrogates with wake validation.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
03
Hydropower
Hydraulics and machines determine efficiency. PIML opportunities: Use flow/turbine twins across heads and dispatch.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
04
Thermal Power Plants
Heat, fluid and equipment dynamics couple. PIML opportunities: Learn bounded component discrepancy for monitoring/control.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
05
Heat Pumps and Refrigeration
Thermodynamic cycles face changing loads. PIML opportunities: Estimate state and optimize with safety limits.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
06
Battery Storage
Electrochemical, thermal and aging states are hidden. PIML opportunities: Use reduced physics observers with uncertainty.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
07
Thermal Energy Storage
Phase/temperature fields determine capacity. PIML opportunities: Learn operators across geometries and cycles.
Model and evidenceGoverning equations, calibrated measurements and held-out operating conditions
08
Hydrogen Systems
Electrolysis, storage and fuel cells interact. PIML opportunities: Model electrochemical/transport dynamics and hazards.
Model and evidenceMechanistic and data-only baselines, uncertainty and independent validation
09
Bioenergy
Feedstock and reaction variability affect conversion. PIML opportunities: Use kinetic hybrids with batch validation.
Model and evidenceGeometry, material or system parameters, sensor data and physical residuals
10
Industrial Heat Integration
Process streams can exchange energy. PIML opportunities: Use balance- and pinch-informed optimization.
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 Smart and Sustainable Energy 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 Smart and Sustainable Energy 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 Smart and Sustainable Energy 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 Smart and Sustainable Energy.
Read publication or record This source is included in the Smart and Sustainable Energy 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 Smart and Sustainable Energy.
Read publication or record This source is included in the Smart and Sustainable Energy 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 Smart and Sustainable Energy.
Read publication or record This source is included in the Smart and Sustainable Energy 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 Smart and Sustainable Energy.
Read publication or record This source is included in the Smart and Sustainable Energy 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 Smart and Sustainable Energy.
Read publication or record This source is included in the Smart and Sustainable Energy 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 Smart and Sustainable Energy.
Read publication or record Build an interdisciplinary team
Where Smart and Sustainable Energy 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 Smart and Sustainable Energy.
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. Smart and Sustainable Energy 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 Smart and Sustainable Energy 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.