Connectionists: [Deadline extended to 7 December 2020] AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physics Sciences (AAAI-MLPS 2021)

[Jonghyun Harry Lee]

***Apologies for cross-posting + extended deadline***

*AAAI Spring Symposium on Combining  Artificial Intelligence and Machine
Learning with Physics Sciences (MLPS)*

March 22-24, 2021, Virtual (due to the COVID-19 situation) Stanford
University, Palo Alto, California, USA

https://sites.google.com/view/aaai-mlps

Key Dates
Submission: December 7, 2020, 23:59 PM Pacific Time November 20, 2020,
23:59 GMT
Notification: January 15, 2021
Symposium: March 22-24, 2021

With recent advances in scientific data acquisition and high-performance
computing, Artificial Intelligence (AI) and Machine Learning (ML) have
received significant attention from applied mathematics and physics science
community. From successes reported by industry, academia, and research
communities, we observe that AI and ML have great potential to leverage
scientific domain knowledge to support new scientific discoveries and
enhance the development of physical models for complex natural and
engineering systems.

For example, deep learning supports discovery of new materials and
high-energy physics from numerous computer simulations and experiments and
let us learn low-dimensional manifolds underlying the acquired data in
order to represent the system of interest parsimoniously and effectively.
ML has offered new insights on adaptive numerical discretization schemes
and numerical solvers, which are clearly distinct from traditional
mathematical theories. AI also provides a new way of generalizing
constitutive physics laws based on big scientific data sets.

Despite the progress, there are still many open questions. Our current
understanding is limited regarding how and why AI/ML work and why they can
be predictive. AI has been shown to outperform traditional methods in many
cases especially with high-dimensional, inhomogeneous data sets. However, a
rigorous understanding of when AI/ML is the right approach is largely
lacking: for what class of problems, underlying assumptions, available data
sets, and constraints are these new methods best suited? The lack of
interpretability in AI-based modeling and related scientific theories makes
them insufficient for high-impact, safety-critical applications such as
medical diagnoses, national security, and environmental contamination. With
transparency and a clear understanding of the data-driven mechanism,
desirable properties of AI should be best utilized to extend current
methods in physical and engineering modeling. Handling expensive training
costs and large memory requirements for ever-increasing scientific data
sets becomes also important to guarantee scalable science machine learning.

This symposium will aim to present the current state of the art and
identify opportunities and gaps in AI/ML-based physics science. The
symposium will focus on challenges and opportunities for increasing the
scale, rigor, robustness, and reliability of physics-informed AI necessary
for routine use in science and engineering applications and discuss
potential researcher-AI collaborations to significantly advance diverse
scientific areas and transform the way science is done.

Topics:
Authors are strongly encouraged to present papers that combine and blend
physical knowledge and artificial intelligence/machine learning algorithms.
Topics of interest include but are not limited to the following:

1. Artificial intelligence/machine learning framework that can seamlessly
synthesize models, governing equations and data
2. Approaches to encode scientific knowledge in machine learning method and
architecture
3. Architectural and algorithmic improvements for scalable physics-informed
learning
4. Stability and error analysis for physics-informed learning
5. Software development facilitating the inclusion of physics domain
knowledge in learning
6. Discovery of physically interpretable laws from data
7. Applications incorporating domain knowledge into machine learning

Submission guideline:

We solicit extended abstracts, full papers, and poster abstracts on topics
related to the above and can include recent or ongoing research, surveys,
and business/use cases. Extended abstracts (2 to 4 pages) and full papers
(up to 6 pages) will be peer-reviewed. Posters can be proposed by
submitting an abstract (1 to 2 pages).

All submissions should follow the AAAI format in the Author Kit
<https://www.aaai.org/Publications/Templates/AuthorKit21.zip>, will be
handled through EasyChair <https://easychair.org/conferences/?conf=sss21> (
https://easychair.org/conferences/?conf=sss21) and the review will be
double-blind to ensure academic integrity. Accepted extended abstracts and
full papers shall be published in an open access proceedings site. More
details can be found in https://easychair.org/cfp/AAAI_MLPS_2021

Invited Speakers:
Animashree Anandkumar, Caltech/NVIDIA
Nathan Kutz, University of Washington
More invited speakers are to be announced

Organizing Committee:
Jonghyun Harry Lee, University of Hawai'i at Manoa
Eric F. Darve, Stanford University
Peter K. Kitanidis, Stanford University
Michael W. Mahoney, University of California, Berkeley
Anuj Karpatne, Virginia Tech
Matthew W. Farthing, U.S. Army Engineer Research and Development Center
Tyler Hesser, U.S. Army Engineer Research and Development Center

Program Committee:
Kevin Carlberg, Facebook
Marta D'Elia, Sandia National Laboratories
Ramakrishnan Kannan, Oak Ridge National Laboratory
Paris Perdikaris, University of Pennsylvania
Sanghyun Lee, Florida State University
Chris Rackauckaus, MIT
Peter Sadowski, University of Hawai'i at Manoa
Nathaniel Trask, Sandia National Laboratories
Hongkyu Yoon, Sandia National Laboratories
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