========================================================== [AI4S]: The 5th Workshop on Artificial Intelligence and Machine Learning for Scientific Applications To be held in conjunction with SC24 Monday, 18 November 2024, 9:00 am - 5.30 pm EST Atlanta, GA, USA Website: https://urldefense.us/v3/__https://ai4s.github.io/__;!!G_uCfscf7eWS!bsazs36r... ========================================================== -------------- Overview -------------- The purpose of this workshop is to bring together computer scientists and domain scientists from academia, government, and industry to share recent advances in the use of AI/ML in various scientific applications, introduce new scientific application problems to the broader community, and stimulate tools and infrastructures not only to support the application of AI/ML in scientific applications but also effectively utilize existing and future HPC systems for AI/ML-based scientific applications The workshop will be organized as a series of plenary talks based on peer-reviewed paper submissions accompanied by keynotes from distinguished researchers in the area and a panel discussion. We encourage participation and submissions from universities, industry, and DOE National Laboratories. Artificial intelligence (AI)/machine learning (ML) is a game-changing technology that has shown tremendous advantages and improvements in algorithms, implementations, and applications. “AI for Science” broadly refers to designing future methods and scientific opportunities that use computational learning and machine intelligence. This includes the development and application of AI methods (e.g., machine learning, deep learning, statistical methods, data analytics, automated control, and related areas). We have seen many successful stories that AI methods are used to predict extreme weather events, identify exoplanets in trillions of sky pixels, accelerate numerical solvers in fluid simulation, design better materials and processes, accelerate drug discovery, explore the mysteries of the universe, and drive an array of scientific discoveries. However, there are a number of problems remaining to be studied to enhance the usability of AI/ML to scientific applications by leveraging HPC systems. For example, how to systematically and automatically apply AI/ML to scientific applications? How to incorporate domain knowledge (e.g., conservation laws, invariants, causality and symmetries) into AI/ML models? How to make the models interpretable and robust for HPC? How to make AI/ML more approachable to the HPC community? How to effectively utilize extreme scale HPC systems and novel AI accelerators for AI/ML? Addressing the above problems will bridge the gap between AI/ML and scientific applications and enable wider employment of AI/ML in HPC. --------------------- Call for Papers --------------------- We solicit research papers in the following topic areas, but not be limited to: - Innovative AI/ML models to analyze, accelerate, or improve performance of scientific applications in terms of execution time and simulation accuracy; - Using HPC systems to accelerate AI/ML training and inferences on large scientific data sets; - Research challenges while using large-scale HPC systems for AI/ML; - Innovative methods to incorporate complex constraints imposed by physical principles to scientific applications; - Workflow of applying AI/ML to scientific applications; - Innovative methods to completely or partially replace first-order computation with efficient AI/ML models; - Tools and infrastructure to improve the usability of AI/ML to scientific applications; - Performance characterization and study on the possibility of using AI/ML to specific scientific applications; - Innovative methods to make AI models interpretable and robust for scientific applications; - Performance evaluation of emerging AI accelerators for scientific ML workloads; - Use of Generative AI to advance the scientific frontier; - Tools and approaches to increase generative AI trustworthiness; - Approaches to address scaling issues associated with Large Language Models. ------------------ Submissions ----------------- Authors are invited to submit manuscripts in English structured as technical papers up to 6 pages 2-column pages (U.S. letter – 8.5″x11″), excluding the bibliography, using the ACM proceedings template. Latex users, please use the “sigconf” option (use of the “review” option is recommended but not required). The manuscripts are single-blind. Word authors can use the “Interim Layout”. Submissions not conforming to these guidelines may be returned without review. All manuscripts will be peer-reviewed and judged on correctness, originality, technical strength, and significance, quality of presentation, and interest and relevance to the workshop attendees. Submitted papers must represent original unpublished research that is not currently under review for any other conference or journal. Papers not following these guidelines will be rejected without review and further action may be taken, including (but not limited to) notifications sent to the heads of the institutions of the authors and sponsors of the conference. Submissions received after the due date, exceeding length limit, or not appropriately structured may also not be considered. At least one author of an accepted paper must register for and attend the workshop. Authors may contact the workshop organizers for more information. Papers should be submitted electronically at: https://urldefense.us/v3/__https://submissions.supercomputing.org__;!!G_uCfs... , SC24 Workshop: AI4S'24: Workshop on Artificial Intelligence and Machine Learning for Scientific Applications". The final papers will be published in the SC Workshops Proceedings. ----------------------- Important Dates: ----------------------- Submission: August 9, 2024 (AoE) Notification of acceptance: September 6, 2024 Camera Ready: September 27, 2024 Workshop: November 18, 2024 -------------- Organizers -------------- Gokcen Kestor, Pacific Northwest National Laboratory Dong Li, University of California, Merced Murali Krishna Emani, Argonne National Laboratory ---------------------------- Program Committee --------------------------- TBA