PI-LSTM: Physics-Infused Long Short-Term Memory Network

Singh, Shubhendu Kumar; Yang, Ruoyu; Behjat, Amir; Rai, Rahul; Chowdhury, Souma; Matei, Ion

doi:10.1109/icmla.2019.00015

Cited by 33 publications

(17 citation statements)

References 13 publications

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“…The terminology for referring such architectures is not consistent. The keywords which were used to refer such architectures include 'using prior information' [121], [122], 'physics guided' [123]- [127], 'residual physics' [128]- [131], 'physics based' [132], 'physics fusion' [128], 'physics infused' [133], 'theory guided' [14], [134], 'hybrid' [135]- [137], 'physics informed' [138]- [143], [143]- [146], 'physics constrained' [123], [147], [148] etc. However irrespective of the name of the hybrid model, they share a lot of commonalities.…”

Section: Hybrid Modeling: Infusing Physics Into Data-driven Modelsmentioning

confidence: 99%

“…Such physics-based architectures have found use in environmental data analysis [123], dynamical systems [133], biological systems [133] and image processing [132]. The output of the physics model was used as an additional input feature for a multilayered perceptron for modeling the temperature of a lake [123].…”

Section: B Physics Based Network Architectures (Pbna)mentioning

confidence: 99%

“…The output of the physics model was used as an additional input feature for a multilayered perceptron for modeling the temperature of a lake [123]. The output of the MB was fed into the last layer of an LSTM network along with the unprocessed inputs for modeling an inverted pendulum and growth of cancer cells [133]. The physics-based motion model of aerial vehicles was sandwiched between two RNN modules for multistep prediction of the non-linear dynamics of the aerial vehicles [137].…”

Section: B Physics Based Network Architectures (Pbna)mentioning

confidence: 99%

“…A simple example could be expecting a model that has been trained for classifying the predecessors of human beings (e.g., monkeys, apes, and gorillas) to perform well on classifying human beings. The hybrid models have portrayed excellent extrapolability [133], [200], [215], [224], [225], [253].…”

Section: ) Extrapolabilitymentioning

confidence: 99%

“…Robust models are those that are resistant to noise. Hybrid models have been proven to be more robust to noise than pure MB and ML methods [133].…”

Section: ) Robustness To Noisy Datamentioning

confidence: 99%

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Driven by Data or Derived Through Physics? A Review of Hybrid Physics Guided Machine Learning Techniques With Cyber-Physical System (CPS) Focus

2020

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A multitude of cyber-physical system (CPS) applications, including design, control, diagnosis, prognostics, and a host of other problems, are predicated on the assumption of model availability. There are mainly two approaches to modeling: Physics/Equation based modeling (Model-Based, MB) and Machine Learning (ML). Recently, there is a growing consensus that ML methodologies relying on data need to be coupled with prior scientific knowledge (or physics, MB) for modeling CPS. We refer to the paradigm that combines MB approaches with ML as hybrid learning methods. Hybrid modeling (HB) methods is a growing field within both the ML and scientific communities, and are recognized as an important emerging but nascent area of research. Recently, several works have attempted to merge MB and ML models for the complete exploitation of their combined potential. However, the research literature is scattered and unorganized. So, we make a meticulous and systematic attempt at organizing and standardizing the methods of combining ML and MB models. In addition to that, we outline five metrics for the comprehensive evaluation of hybrid models. Finally, we conclude by shedding some light on the challenges of hybrid models, which we, as a research community, should focus on for harnessing the full potential of hybrid models. An additional feature of this survey is that the hybrid modeling work has been discussed with a focus on modeling cyber-physical systems. INDEX TERMS Cyber-physical systems, deep learning, deep neural networks, hybrid models, model-based, machine learning, physics guided, physics informed, physics prior, theory guided.

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