Workshop Program

AI4S 2020 will be held online on Friday, November 13. The schedule is provided below. All times are in Eastern Standard Time (the timezone of Atlanta, GA, USA).

10:00-10:05AM	Welcome and Intro
10:05-11:00AM	Keynote: Steven Lee (ASCR/DOE) Session chair: Gokcen Kestor
11:00-11:25AM	Paper: Automatic Particle Trajectory Classification in Plasma Simulations (Stefano Markidis, Ivy B. Peng, Artur Podobas, Itthinat Jongsuebchoke, Gabriel Bengtsson, Pawel Herman.) Session chair: Murali Krishna Emani
11:25-11:50AM	Paper: Reinforcement Learning-Based Solution to Power Grid Planning and Operation Under Uncertainties (Xiumin Shang, Lin Ye, Jing Zhang, Jingping Yang, Jianping Xu, Qin Lyu, Ruisheng Diao. ) Session chair: Murali Krishna Emani
11:50AM-12:15PM	Paper: Predictions of Steady and Unsteady Flows using Machine-learned Surrogate Models (Shanti Bhushan) Session chair: Murali Krishna Emani
12:15-12:40PM	Paper: Deep Learning-based Low-dose Tomography Reconstruction with Hybrid-dose Measurements (Ziling Wu, Tekin Bicer, Zhengchun Liu, Vincent De Andrade, Yunhui Zhu, Ian T. Foster. ) Session chair: Murali Krishna Emani
12:40-1:05PM	Paper: How Good Is Your Scientific Data Generative Model? (Yuxin Yang, Ben Gremillion, Xitong Zhang, Youzuo Lin, Brendt Wohlberg, Qiang Guan.) Session chair: Murali Krishna Emani
1:05-2:00PM	Panel (Aparna Chandramowlishwaran (UCI), David Womble (ORNL), and Sivasankaran Rajamanickam (Sandia).) Moderator: Dong Li

Call for Papers

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 to various scientific applications, introduce new scientific application problems to the broader community, and stimulate tools and infrastructures to support the application of AI/ML in 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, implementation, and applications. We have seen many successful stories of applying AI/ML to scientific applications, such as predicting extreme weather events, identifying exoplanets in trillions of sky pixels, and accelerating numerical solvers in fluid simulation. However, there are a number of problems remaining to be studied to enhance the usability of AI/ML to scientific applications. 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? Addressing the above problems will bridge the gap between AI/ML and scientific applications and enable wider employment of AI/ML in HPC.

Topics will include but will 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;

Innovative methods to incorporate complex constraints imposed by physical principles 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;

Workflow of applying AI/ML to scientific applications;

Innovative methods to make AI models interpretable and robust for scientific applications.

Submission

Authors are invited to submit manuscripts in English structured as technical papers up to 8 pages, both of letter size (8.5in x 11in) and including figures, tables, and references. Submissions not conforming to these guidelines may be returned without review. Your paper should be formatted using IEEE conference format which can be found from here The workshop also encourage submitters to include transparency and reproducibility information, using Transparency and Reproducibility Initiative for SC'20 Technical Papers as guideline.

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://submissions.supercomputing.org, choose "SC20 Workshop: AI4S'20: Workshop on Artificial Intelligence and Machine Learning for Scientific Applications".

The final papers are planned to be published through IEEE TCHPC. Published proceedings will be included in the IEEE Xplore digital library.

Important Dates