AAAI-MLPS 2020: AAAI 2020 SPRING SYMPOSIUM: Combining Artificial Intelligence and Machine Learning with Physical Sciences

Країна: США

Місто: Stanford

Тези до: 15.11.2019

Дати: 23.03.20 — 25.03.20

Область наук: Технічні;

Швидкі посилання

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Е-мейл Оргкомітету:

Організатори: Stanford University


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.

List of 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:

  • Artificial intelligence/machine learning framework that can seamlessly synthesize models, governing equations and data
  • Algorithms for scalable physics-informed learning
  • Stability and error analysis for physics-informed learning
  • Software development facilitating the inclusion of physics domain knowledge in learning
  • Applications incorporating domain knowledge into machine learning

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