Functionality of Physics-Informed Neural Networks and Potential Future Impacts on Artificial Intelligence


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Authors

  • Tejas Nair HS of Endeavor-Austin, USA
  • Merve Gokgol HS of Endeavor, Austin, USA

DOI:

https://doi.org/10.31039/plic.2024.11.247

Keywords:

PINNs, Physics-Informed Neural Networks, Embedded Physics Equations, Loss Function

Abstract

Physics-informed neural networks, or PINNs, are indicative of a new approach that involves the use of scientific knowledge, as these programs adhere to laws of physics described by general nonlinear partial differential equations while solving problems that are related to physics. This is accomplished via programming these equations into the loss function, which ensures that the underlying system adheres to these laws. This paper will be discussing how PINNs function and analyze how they make use of physics when solving problems. PINNs can be used to model physical systems and phenomena in the real world, including combustion, quantum mechanics, and the simulation of fluid. The data embedded into the code of PINNs also serves to address the issue some neural networks may have with a lack of important data needed to solve relevant scientific issues. The rules and constraints PINNs have ensures that they will provide more realistic solutions in comparison to alternatives. Lastly, this paper will be discussing the potential future applications of PINN programming and functionality on future artificial intelligence (AI) development. PINNs have the potential to address complex scientific problems in a way that other solutions may not be able to, and as such, they are an important topic of discussion.

References

Cai, S., Mao, Z., Wang, Z., Yin, M., & Karniadakis, G. E. (2021). Physics-informed neural networks (PINNs) for fluid mechanics: a review. Acta Mechanica Sinica, 37(12), 1727–1738. https://doi.org/10.1007/s10409-021-01148-1

Cuomo, S., Di Cola, V. S., Giampaolo, F., Rozza, G., Raissi, M., & Piccialli, F. (2022). Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next. Journal of Scientific Computing, 92(3). https://doi.org/10.1007/s10915-022-01939-z

Mao, Z., Jagtap, A., & Em Karniadakis, G. (2020). Physics-informed neural networks for high-speed flows. Computer Methods in Applied Mechanics and Engineering, 360, 112789. https://doi.org/10.1016/j.cma.2019.112789

Misyris, G. S., Venzke, A., & Spyros Chatzivasileiadis. (2020). Physics-Informed Neural Networks for Power Systems. ArXiv (Cornell University). https://doi.org/10.1109/pesgm41954.2020.9282004

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Published

2024-09-09

How to Cite

Nair, T., & Merve Gokgol. (2024). Functionality of Physics-Informed Neural Networks and Potential Future Impacts on Artificial Intelligence. Proceedings of London International Conferences, (11), 120–124. https://doi.org/10.31039/plic.2024.11.247

Issue

No. 11 (2024): Proceedings of London International Conference

Section

Articles

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Copyright (c) 2024 Tejas Nair, Merve Gokgol

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