Physics-informed ANNs for dynamic, distributed and stochastic systems (SmartANN)

PN 5-6

Project description

We rate two aspects of machine learning (ML) as extremely critical: (1) it ignores the physical process understanding accumulated in past centuries and (2) ML results are too black-box-like for providing logical explanations in building new process understanding. Our goal is that scientific understanding can assist ML, and that ML becomes useful in the quest for new process understanding and providing explanations. To achieve this goal, we design appropriate forms of artificial neural networks (ANNs) with a physics-informed internal structure and hypothesis space. Then, we confine their learning process with additional physics-based knowledge. Finally, we teach them to realize their own tendency for prediction errors and to provide this insight in the form of statistical distributions. This way, we obtain stochastic ML models that are not a black box, have better learning success from less training examples, achieve superior forecasting skills with honest uncertainty intervals. Additionally, they can be used accurately for data assimilation from systems that are understood only in parts. We develop and validate our ideas on a prototypical energy storage device. In this application, we use ML in order to track the state of charge, temperature and pressure, and to predict the state of health and remaining lifetime of the device. The German Aerospace Center (DLR) will provide experimental data from such a prototype, and partners from Project Network 1 will provide existing simulation tools.

Project information

Project title Physics-informed ANNs for dynamic, distributed and stochastic systems (SmartANN)
Project leaders Wolfgang Nowak (Sergey Oladyshkin, Guido Schneider)
Project partners DLR Stuttgart, André Thess (experimental data)
Rainer Helmig, Bernd Flemisch (numerical simulation models)
Project duration October 2019 - March 2023
Project number PN 5-6

Publications PN 5-6 and PN 5-6 (II)

  1. 2024

    1. J. Bartsch, P. Knopf, S. Scheurer, and J. Weber, “Controlling a Vlasov-Poisson Plasma by a Particle-in-Cell Method based on a Monte Carlo Framework,” SIAM Journal on Control and Optimization, vol. 62, no. 4, Art. no. 4, Jul. 2024, doi: 10.1137/23M1563852.
  2. 2023

    1. S. Oladyshkin, T. Praditia, I. Kroeker, F. Mohammadi, W. Nowak, and S. Otte, “The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory,” Neural Networks, vol. 166, pp. 85–104, Sep. 2023, doi: 10.1016/j.neunet.2023.06.036.
    2. C. C. Horuz et al., “Physical Domain Reconstruction with Finite Volume Neural Networks,” Applied Artificial Intelligence, vol. 37, no. 1, Art. no. 1, 2023, doi: https://doi.org/10.1080/08839514.2023.2204261.
  3. 2022

    1. M. Takamoto et al., “PDEBench: An Extensive Benchmark for Scientific Machine Learning,” in 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks, in 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks. 2022.
    2. M. Karlbauer, T. Praditia, S. Otte, S. Oladyshkin, W. Nowak, and M. V. Butz, “Composing Partial Differential Equations with Physics-Aware Neural Networks,” in Proceedings of the 39th International Conference on Machine Learning, in Proceedings of the 39th International Conference on Machine Learning. Baltimore, USA, 2022.
    3. C. C. Horuz et al., “Inferring Boundary Conditions in Finite Volume Neural Networks,” in International Conference on Artificial Neural Networks and Machine Learning -- ICANN 2022, E. Pimenidis, P. Angelov, C. Jayne, A. Papaleonidas, and M. Aydin, Eds., in International Conference on Artificial Neural Networks and Machine Learning -- ICANN 2022. Cham: Springer International Publishing, 2022, pp. 538–549. doi: https://doi.org/10.1007/978-3-031-15919-0_45.
    4. T. Praditia, M. Karlbauer, S. Otte, S. Oladyshkin, M. V. Butz, and W. Nowak, “Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network,” Water Resources Research, vol. 58, no. 12, Art. no. 12, 2022, doi: 10.1029/2022WR033149.
  4. 2021

    1. T. Praditia, M. Karlbauer, S. Otte, S. Oladyshkin, M. Butz, and W. Nowak, “Finite Volume Neural Network: Modeling Subsurface Contaminant Transport,” in Deep Learning for Simulation ICLR Workshop 2021, in Deep Learning for Simulation ICLR Workshop 2021. 2021. [Online]. Available: https://simdl.github.io/files/33.pdf
    2. S. Xiao, T. Praditia, S. Oladyshkin, and W. Nowak, “Global sensitivity analysis of a CaO/Ca(OH)2 thermochemical energy storage model for parametric effect analysis,” Applied Energy, vol. 285, p. 116456, 2021.
    3. S. Scheurer et al., “Surrogate-based Bayesian Comparison of Computationally Expensive Models: Application to Microbially Induced Calcite Precipitation,” Computational Geosciences, vol. 25, pp. 1899–1917, 2021.
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