Paper Types and Presentation Policy
Call for Abstracts
We invite researchers, academicians, industry professionals and students to submit abstracts for inclusion in the conference program and featured publication. Topics of interest for submission include, but are not limited to, the application of computational techniques in addressing various problems across engineering and science domains. Abstracts should clearly express the research problem, computational approach and potential contributions to the respective field. We welcome submissions that demonstrate novel computational approaches, interdisciplinary collaborations or impactful real-world applications of these techniques.
Full Paper (Presentation and Publication)
Authors selecting this category are required to submit the full paper within a month of abstract acceptance for publication in the conference proceedings/journals. The submitted papers will undergo a peer review process.
Extended Abstract (Presentation only)
Authors selecting this category need not submit the full paper; abstracts will be published in the conference program or book of abstracts.
Presentation policy Only papers presented at the conference will receive conference presentation certificates and will be considered for the publication process.
Focused on Frontiers
Conference Tracks
- Topics of interest for submission include, but are not limited to:
- Advanced simulation applications
- AI and ML in modeling and simulations
- Computational techniques for system modeling
- Data-driven modeling in engineering
- Digital twins in Engineering and Science
- Modeling dynamic and nonlinear systems
- Multi-scale and multi-physics modeling
- Predictive modeling techniques
- Simulations for energy and sustainability
- Advances in optimization techniques
- Evolutionary and nature-inspired algorithms
- Hybrid optimization for complex systems
- Linear and nonlinear optimization
- Machine learning in optimization
- Multi-objective optimization
- Optimization in decision-making
- Optimization methods in science
- Stochastic optimization for uncertainty