CALL FOR PAPER

The International Symposium on AI for Science and Interdisciplinary Discovery (ASID 2026) aims to present recent research advances and technological innovations in artificial intelligence–driven scientific research and interdisciplinary discovery. The symposium provides an international forum for researchers, scientists, engineers, and interdisciplinary scholars to exchange ideas, discuss emerging methodologies, and explore collaborative opportunities at the intersection of AI and scientific inquiry.

ASID 2026 welcomes original research papers, case studies, and comprehensive reviews that contribute to the theoretical foundations, methodological frameworks, and practical applications of AI in scientific discovery and cross-disciplinary research. Topics of interest include, but are not limited to:

1. Foundations and Architectures of Artificial Intelligence

  • Adaptive learning models and self-organizing neural architectures
  • Symbolic reasoning and hybrid intelligence frameworks
  • Explainable and interpretable machine learning systems
  • Human-centered and cognitive-inspired AI design

2. AI for Scientific Discovery and Modeling

  • Machine learning for scientific modeling and hypothesis generation
  • AI-assisted discovery in physics, chemistry, biology, and materials science
  • Data-driven simulation, surrogate modeling, and uncertainty quantification
  • Integration of AI with experimental and theoretical scientific workflows

3. Interdisciplinary AI and Data-Driven Research

  • AI-enabled interdisciplinary research methodologies
  • Large-scale scientific data analysis and knowledge extraction
  • Multimodal data fusion across scientific domains
  • Cross-domain representation learning and transfer learning

4. Autonomous Systems and Intelligent Scientific Instruments

  • Autonomous experimentation and self-driving laboratories
  • Intelligent sensing, measurement, and control systems
  • Robotics and automation for scientific research
  • Closed-loop optimization in experimental and computational science

5. High-Performance Computing and AI Integration

  • AI acceleration for high-performance and exascale computing
  • Hybrid AI–HPC architectures and workflow optimization
  • Distributed, cloud, and edge computing for scientific applications
  • Scalable AI algorithms for large-scale scientific problems

6. Trustworthy, Ethical, and Responsible AI for Science

  • Reliability, robustness, and validation of scientific AI models
  • Explainability and transparency in AI-driven scientific results
  • Ethical considerations in AI-enabled scientific research
  • Reproducibility, data governance, and responsible AI practices