Organiser:
Biography: Dr. Talha Anwar is a faculty member in the School of Science at Walailak University, Thailand, and a Fellow of the Higher Education Academy (FHEA). He earned his Ph.D. in Applied Mathematics and subsequently completed a two-year postdoctoral fellowship at King Mongkut's University of Technology Thonburi (KMUTT), Bangkok, Thailand. He also served as a Visiting Researcher at Friedrich-Alexander University Erlangen–Nürnberg (FAU), Germany. He has been invited to deliver over 15 keynote, plenary, and invited lectures at international conferences, reflecting the recognition of his contributions to the field. Beyond research, he has actively contributed to international academic initiatives, including the SEA Teacher Program, and played a role in the development of an Erasmus+ mobility proposal aimed at strengthening international collaboration and academic exchange. He has published in leading international journals and serves as a reviewer and editorial member for several well-reputed international journals. Dr. Talha Anwar's research focuses on artificial intelligence, physics-informed machine learning, computational modeling, and scientific computing for sustainable engineering applications. His work integrates AI with mathematical modeling and computational fluid dynamics to develop efficient, reliable, and data-driven solutions for complex real-world challenges in energy, environmental sustainability, and advanced engineering systems.
Introduction:
Artificial intelligence is reshaping
computational science and engineering by
enabling more efficient modeling,
simulation, optimization, and data-driven
decision-making across diverse scientific
and engineering disciplines. By integrating
machine learning with mathematical modeling,
numerical methods, and high-performance
computing, AI is accelerating scientific
discovery while improving the accuracy,
scalability, and reliability of
computational approaches. This Special
Session aims to provide an interdisciplinary
forum for researchers and practitioners to
explore recent advances, emerging
challenges, and innovative applications of
AI in computational science and engineering.
The session welcomes theoretical,
computational, and application-oriented
contributions on topics including
physics-informed machine learning,
AI-assisted numerical simulation, scientific
machine learning, surrogate and
reduced-order modeling, digital twins,
optimization, uncertainty quantification,
computational fluid dynamics, computational
mechanics, materials modeling, and
intelligent data-driven engineering systems.
Contributions addressing the integration of
AI with traditional computational methods
for solving complex, real-world problems in
energy, manufacturing, healthcare,
environmental science, and other engineering
domains are particularly encouraged. By
bringing together researchers from applied
mathematics, computer science, artificial
intelligence, computational science, and
engineering, this session seeks to foster
interdisciplinary collaboration, promote the
development of trustworthy and efficient
AI-enhanced computational methods, and
advance intelligent technologies that
support sustainable scientific and
engineering innovation.
Topics:
The session invites original research
contributions, case studies, and review
articles on topics including, but not
limited to:
• AI-Assisted Computational Modeling
• Digital Twins and Intelligent Simulation
• Data-Driven Modeling and Hybrid Physics–AI
Models
• Machine Learning for Computational Mechanics
• AI-Based Optimization
• Uncertainty Quantification and Explainable
AI in Scientific Computing
• AI for Materials Science
• AI-Supported Decision Making
• AI Applications in Energy Systems and
Renewable Energy
• AI for Environmental and Climate Modeling
• AI for Smart Manufacturing and Industrial
Engineering
• Mathematical Modeling with Artificial
Intelligence
• Deep Learning for Engineering Applications
• Reinforcement Learning for Engineering
Optimization
• Emerging Applications of AI in Computational
Science and Engineering
• Role of AI in Industiral Sustainability
• Trustworthy, Interpretable, and Responsible
• AI in Computational Science
• Machine learning techniques for
Computational Fluid Dynamics (CFD)
• AI in Biomedical and Healthcare Engineering
Simulations