A-SMART Lab works with industry, government agencies, research institutions, and academic partners to address complex challenges in energy, infrastructure, and engineering through AI-enabled modeling, optimization, uncertainty analysis, and decision intelligence.
Our goal is to translate advanced computational methods into practical insights, models, tools, and decision-support capabilities that help partners evaluate technologies, understand uncertainty, compare alternatives, and make better engineering and investment decisions.
How We Can Help
AI-Enabled Engineering and Analytics
Develop machine-learning, scientific-ML, surrogate-modeling, and AI-assisted workflows for complex engineering data and systems.
Potential applications include:
- Predictive modeling and engineering analytics
- Physics-informed and hybrid AI
- Surrogate and reduced-order models
- Large language model applications
- Model validation and interpretability
- AI-assisted engineering workflows
Optimization and Decision Intelligence
Support complex planning and operational decisions involving competing objectives, uncertainty, risk, and economic constraints.
Capabilities include:
- Mathematical and multi-objective optimization
- Uncertainty quantification
- Sensitivity and scenario analysis
- Risk-informed decision-making
- Techno-economic analysis
- Infrastructure planning and resource allocation
CCUS and Low-Carbon Infrastructure
Support the planning, evaluation, and optimization of carbon-management and low-carbon infrastructure systems.
Areas include:
- CO₂ pipeline and maritime transport
- Integrated capture–transport–storage networks
- CO₂ storage and injection
- Infrastructure reuse
- Regional CCUS planning
- Hydrogen and emerging low-carbon infrastructure
- Techno-economic and risk assessment
Subsurface and Geoenergy Systems
Develop models and decision-support frameworks for subsurface systems characterized by geological uncertainty and computational complexity.
Applications include:
- Geological CO₂ storage
- Geothermal energy
- Underground hydrogen storage
- Reservoir modeling
- Injectivity and storage performance
- Subsurface risk and uncertainty
- Optimization of subsurface operations
Ways to Collaborate
We support different forms of engagement depending on the technical challenge, project scope, and partner needs.
- Collaborative research — Joint research projects addressing strategic engineering and scientific challenges.
- Sponsored applied research — Focused studies developed around an organization’s technical or decision-making needs.
- Technical and feasibility studies — Evaluation of technologies, infrastructure configurations, scenarios, and operating strategies.
- Computational model and software development — Research prototypes, simulation tools, optimization frameworks, analytical workflows, and decision-support systems.
- Professional training and workshops — Customized training in AI-enabled engineering, optimization, CCUS, data analytics, and related topics.
- Student-industry projects — Final-year projects, internships, interdisciplinary team projects, and other applied student engagements.
Potential Project Outcomes
Depending on the collaboration, project outputs may include:
- Engineering and techno-economic models
- AI and machine-learning models
- Optimization and decision-support frameworks
- Scenario and uncertainty analyses
- Research software and computational prototypes
- Technical reports and feasibility assessments
- Data visualization and analytical dashboards
- Workshops and professional training
- Research publications and joint proposals
Research Software & Computational Tools
A-SMART Lab has experience developing and contributing to research software and decision-support platforms, scientific Python workflows, simulation tools, optimization frameworks, and reproducible computational pipelines.
Why Collaborate with A-SMART?
Our approach combines:
AI + Physics + Optimization + Uncertainty + Engineering Decision-Making
This allows us to address problems that require more than prediction alone—particularly where engineering constraints, economics, uncertainty, risk, and long-term planning must be considered together.
We aim to bridge academic research and practical deployment, helping partners move from data and models toward actionable engineering decisions.
Discuss a Collaboration
If your organization is exploring a challenge involving AI-enabled engineering, optimization, CCUS, energy infrastructure, geoenergy, techno-economic analysis, or decision support, we welcome discussions on potential collaboration.
Note: Collaborative research, sponsored projects, professional training, and other external engagements are subject to applicable Singapore Institute of Technology policies, approvals, and contracting procedures.