[hpc-announce] AI4S Workshop at SC26

[Murali Emani]

==========================================================
[AI4S]: The 8th Workshop on Artificial Intelligence and Machine
Learning for Scientific Applications

To be held in conjunction with SC26

Sunday, November 15, 9 AM-5.30 PM
Chicago, IL, USA

Website: https://urldefense.us/v3/__https://ai4s.github.io/__;!!G_uCfscf7eWS!YAJuZahNeUyTbJdaYI01gq1yxSpP3eNIid9aDkbFhsUkDGJg_dyvmr9ir6BlwuegJEPkCzhjv_PRmoXuZz9wb6MgPw$ 
==========================================================

--------------
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) 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.

---------------------
Call for Papers
---------------------
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.


------------------
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 IEEE proceedings
template. The manuscripts are
single-blind. 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!YAJuZahNeUyTbJdaYI01gq1yxSpP3eNIid9aDkbFhsUkDGJg_dyvmr9ir6BlwuegJEPkCzhjv_PRmoXuZz_26c_IIA$ , SC26 Workshop: AI4S'26:
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 8, 2026 (AoE)
Notification of acceptance: September 4, 2026
Camera Ready: September 25, 2026
Workshop: November 15, 2026

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

----------------------------
Program Committee
---------------------------

TBA