Infrastructure Resilience
Structural monitoring, risk-informed maintenance, disaster response, interdependent networks and service restoration.
About AIRES · Aims & Scope
AI research, grounded in the way engineering systems work.
AIRES welcomes rigorous research on how artificial intelligence can improve the resilience, dependability and adaptive performance of engineered systems.
Research areas
Five connected areas of engineering resilience.
Work may be theoretical, computational, experimental or field-based when its evidence is clearly identified and supports its conclusions.
Distributed Energy Systems
Microgrids, storage, virtual power plants, robust dispatch, stability-aware control and recovery.
Manufacturing Reliability
Fault diagnosis, predictive maintenance, process quality, production recovery and human-machine systems.
Materials Processing & Performance
Process–structure–property modelling, defect control, manufacturing and engineering service performance.
Cyber-Physical Security
Industrial control, adversarial sensing, safe autonomy, threat models and physical recovery.
Evidence expectations
Make the claim traceable to the evidence.
A benchmark result alone does not establish field performance. Submissions should explain where evidence comes from and what it can support.
01 Define the system, constraints, failure modes and intended use.
02 Report data provenance, assumptions and model configuration.
03 Use credible baselines; report uncertainty and failure cases.
04 Explain limits and what can be reproduced.
Prepare a submissionExplore current research in AIRES.
Article records will be listed by annual volume after publication.