Connectionists: Call for Papers: AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physics Sciences (MLPS)

[Jonghyun Harry Lee]

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

March 23-25, 2020 @Stanford University, Palo Alto, California, USA

https://sites.google.com/view/aaai-mlps
Key Dates: November 15th, 2019, 23:59 GMT
Notification: December 14th, 2019
Symposium: March 23-25, 2020

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

For example, deep learning can support discovery of new materials and
high-energy physics from numerous computer simulations and experiments to
allow us to 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 this 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. That is, 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 and remediation. With transparency and a clear
understanding of the data-driven mechanisms, the desirable properties of AI
should be best utilized to extend current methods in modeling for physics
and engineering problems. At the same time, handling expensive training
costs and large memory requirements for ever-increasing scientific data
sets is becoming more and more 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 modeling and
analysis. 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 bridging AI and engineering research 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. Algorithms for scalable physics-informed learning
3. Stability and error analysis for physics-informed learning
4. Software development facilitating the inclusion of physics domain
knowledge in learning
5. 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, will be
handled through EasyChair (https://easychair.org/conferences/?conf=sss20)
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_2020

Organizing Committee:
Jonghyun Harry Lee, University of Hawai'i at Manoa, Honolulu, USA
Eric F. Darve, Stanford University, Stanford, USA
Peter K. Kitanidis, Stanford University, Stanford, USA
Matthew Farthing, U.S. Army Engineer Research and Development Center
Tyler Hesser, U.S. Army Engineer Research and Development Center

Invited Speakers:
George Karnidakis, Brown University
Marco Pavone, Stanford University
Lexing Ying, Stanford University
Paris Perdikaris, University of Pennsylvania
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