In its 11th year ISAV is expanding in scope and technical focus, and now invites full paper submissions up to 10 pages (including references) and works on in situ AI/ML training or inference. ISAV also continues to invite short papers (5 page + 1 page references) and lightning talk abstracts (1 page).

Full papers should present research results, identity opportunities or challenges, or present case studies/best practices for in situ methods. Short papers may also document late breaking ideas & early progress on novel concepts. Lightning talks are encouraged to present preliminary works or ideas to foster discussion with the community. Full and short papers will appear in the workshop proceedings and authors will be invited to give an oral presentation at the workshop; lightning talks will be invited to give brief oral presentations at the workshop.

Submissions of all types may identify opportunities, challenges and best practices for in situ AI/ML, in situ analysis and in situ visualization. They may propose new methods and techniques, provide positions, or experience reports on in situ analysis, learning and visualization. Areas of interest for ISAV include, but are not limited to:

Methods, Algorithms and Synthesis between HPC & ML: In situ analysis (feature detection, data reduction/compression, data summarization, ML training) and scientific visualization using data-driven, surrogate-assisted, statistical, temporal, geometric, or time-varying methods.
Applications and Workflows: Applications (simulations, data processing, scientific user facilities) and integrations into digital twins. Workflows for supporting complex in situ processing pipelines (incl. enabling accelerated post-processing and elasticity), their resilience (error detection, data congestion, fault recovery) and reproducibility.
Scalability Requirements: Scalability, resource utilization, data flow, and simplified access to extreme heterogeneous resources. Real-time coupling of data (modeled or measured), surrogates and algorithms.
Case Studies, Data Sources and Best Practices & Usability: Examples/case studies of solving a specific science challenge with in situ methods/infrastructure. In situ methods/systems applied to data from simulations, and/or observations/experiments. Deployments & software engineering.
Software Evolution & Standardization: In situ libraries from research prototypes to production quality. Challenges, opportunities, gaps in existing capabilities. API designs and development of community standards.
Enabling Hardware & Emerging Architectures: Hardware & emerging system architectures that provide opportunities for in situ processing. Efficient use of hardware accelerators and heterogeneous architectures, incl. HPC, Data Center or Edge.