[hpc-announce] AI4S Workshop at SC25

[Murali Emani]

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[AI4S]: The 6th Workshop on Artificial Intelligence and Machine Learning
for Scientific Applications

To be held in conjunction with SC25
Monday, November 17, 9 AM-5.30 PM Central Daylight Time
St. Louis, MO, USA

Website: https://urldefense.us/v3/__https://ai4s.github.io/__;!!G_uCfscf7eWS!cHuTS72Ow7mihK9mgXn7bafViOZtZLbzyFlWgE0KANulNiIQtAITogcSylI8eXJC0BoIjHwL1qLyK2bliIq8FIWjdA$ 
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Overview
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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) and Machine Learning (ML) are transforming
scientific discovery, driving breakthroughs in climate modeling, materials
discovery, astrophysics, drug development, and many other domains. AI for
Science (AI4S) focuses on developing and applying computational learning
and machine intelligence to accelerate innovation in scientific research.
Recent years have witnessed AI-driven advances that have reshaped the
landscape of scientific research. AI methods have been successfully applied
to predict extreme weather events with greater accuracy, identify
exoplanets among trillions of sky pixels, accelerate numerical solvers for
fluid dynamics and physics-based simulations, design novel materials and
chemical processes, improve drug discovery pipelines, and help uncover
fundamental insights into the universe. At the same time, generative AI is
revolutionizing not only scientific computing but also broader societal
applications. However, despite these advancements, several fundamental
challenges remain in effectively integrating AI/ML into scientific
workflows, particularly when leveraging high-performance computing (HPC)
resources. One of the most pressing questions is how to systematically and
automatically apply AI/ML techniques to complex scientific applications
while ensuring reliability, interpretability, and efficiency. The
integration of domain knowledge, such as conservation laws, invariants,
causality, and symmetries, remains an open problem that requires deeper
exploration. Additionally, enhancing the robustness of AI models for HPC
environments and making them interpretable for scientific analysis is a
crucial step toward broader adoption. Foundation models designed for
scientific applications need further refinement to meet domain-specific
requirements, and significant efforts are needed to reduce the energy cost
of large-scale AI training. Ensuring that AI/ML frameworks are approachable
and efficient for the broader HPC community is another important challenge,
along with effectively utilizing extreme-scale HPC systems and emerging AI
accelerators to unlock new scientific possibilities.

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Call for Papers
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We solicit research papers in the following topic areas, but not be limited
to:

- Advancing scientific discovery and development workflows through
generative AI models;
- Strategies for reducing the energy consumption of AI model training and
inference;
- Investigating the impact of quantization and reduced precision techniques
on the accuracy and correctness of AI-driven scientific applications;
- Novel AI/ML approaches for improving execution time, efficiency, and
simulation accuracy;
- Characterizing the impact, accuracy, and effectiveness of AI/ML for
scientific workloads;
- Leveraging HPC systems to accelerate training and inference of AI/ML
models on large-scale scientific datasets;
- Addressing scalability, optimization, and efficiency issues when
deploying AI/ML on extreme-scale HPC platforms;
- Methods for integrating domain knowledge, including physical laws and
constraints, into AI-driven scientific modeling;
- Replacing or augmenting traditional numerical methods with AI/ML models
for computational efficiency;
- Tools, frameworks, and infrastructure to enhance the usability and
accessibility of AI in scientific applications;
- Methods to improve interpretability, explainability, and robustness of AI
models for critical scientific applications;
- Performance evaluation and benchmarking of emerging AI accelerators
(e.g., GPUs, TPUs, FPGAs, neuromorphic computing) for scientific workloads;


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Submissions
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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_uCfscf7eWS!cHuTS72Ow7mihK9mgXn7bafViOZtZLbzyFlWgE0KANulNiIQtAITogcSylI8eXJC0BoIjHwL1qLyK2bliIqYIY7N7Q$ , SC25 Workshop: AI4S'25:
Workshop on Artificial Intelligence and Machine Learning for
Scientific Applications".

The final papers will be published in the SC Workshops Proceedings.


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Important Dates:
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Submission: August 8, 2025 (AoE)
Notification of acceptance: September 5, 2025
Camera Ready: September 26, 2025
Workshop: November 17, 2025

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Organizers
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Gokcen Kestor, Pacific Northwest National Laboratory
Dong Li, University of California, Merced
Murali Krishna Emani, Argonne National Laboratory

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Program Committee
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TBA