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Poster
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
Wang Zhang · Lily Weng · Subhro Das · Alexandre Megretsky · Luca Daniel · Lam Nguyen

Wed Jul 26 02:00 PM -- 03:30 PM (PDT) @ Exhibit Hall 1 #109
in Poster Session 3 »
Event URL: https://github.com/wz16/concernet »
Deep neural networks (DNN) have shown great capacity of modeling a dynamical system; nevertheless, they usually do not obey physics constraints such as conservation laws. This paper proposes a new learning framework named $\textbf{ConCerNet}$ to improve the trustworthiness of the DNN based dynamics modeling to endow the invariant properties. $\textbf{ConCerNet}$ consists of two steps: (i) a contrastive learning method to automatically capture the system invariants (i.e. conservation properties) along the trajectory observations; (ii) a neural projection layer to guarantee that the learned dynamics models preserve the learned invariants. We theoretically prove the functional relationship between the learned latent representation and the unknown system invariant function. Experiments show that our method consistently outperforms the baseline neural networks in both coordinate error and conservation metrics by a large margin. With neural network based parameterization and no dependence on prior knowledge, our method can be extended to complex and large-scale dynamics by leveraging an autoencoder.
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Author Information

Wang Zhang (MIT)
Lily Weng (UCSD)
Subhro Das (IBM Research)

Subhro Das is a Research Staff Member and Manager at the MIT-IBM AI Lab, IBM Research, Cambridge MA. As a Principal Investigator (PI), he works on developing novel AI algorithms in collaboration with MIT. He is a Research Affiliate at MIT, co-leading IBM's engagement in the MIT Quest for Intelligence. He serves as the Chair of the AI Learning Professional Interest Community (PIC) at IBM Research. His research interests are broadly in the areas of Trustworthy ML, Reinforcement Learning and ML Optimization. At the MIT-IBM AI Lab, he works on developing novel AI algorithms for uncertainty quantification and human-centric AI systems; robust, accelerated, online & distributed optimization; and, safe, unstable & multi-agent reinforcement learning. He led the Future of Work initiative within IBM Research, studying the impact of AI on the labor market and developing AI-driven recommendation frameworks for skills and talent management. Previously, at the IBM T.J. Watson Research Center in New York, he worked on developing signal processing and machine learning based predictive algorithms for a broad variety of biomedical and healthcare applications. He received MS and PhD degrees in Electrical and Computer Engineering from Carnegie Mellon University in 2014 and 2016, respectively, and Bachelors (B.Tech.) degree in Electronics & Electrical Communication Engineering from Indian Institute of Technology Kharagpur in 2011.

Alexandre Megretsky (Massachusetts Institute of Technology)
Luca Daniel (Massachusetts Institute of Technology)
Lam Nguyen (IBM Research, Thomas J. Watson Research Center)

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