Real-time Health Monitoring of Heat Exchangers using Hypernetworks and PINNs
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Abstract (arXiv)
We demonstrate a Physics-informed Neural Network (PINN) based model for real-time health monitoring of a heat exchanger, that plays a critical role in improving energy efficiency of thermal power plants. A hypernetwork based approach is used to enable the domain-decomposed PINN learn the thermal behavior of the heat exchanger in response to dynamic boundary conditions, eliminating the need to re-train. As a result, we achieve orders of magnitude reduction in inference time in comparison to existing PINNs, while maintaining the accuracy on par with the physics-based simulations. This makes the approach very attractive for predictive maintenance of the heat exchanger in digital twin environments.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 4 of its 16 references that are in the map. First posted on arXiv 20 Dec 2022.
Builds on
4 of 16 references.
References · 4
- 2020Transfer learning based multi-fidelity physics informed deep neural networkcited by 68
- 2021One-Shot Transfer Learning of Physics-Informed Neural Networkscited by 49
- 2021HyperPINN: Learning parameterized differential equations with physics-informed hypernetworkscited by 39
- 2021Mosaic Flows: A Transferable Deep Learning Framework for Solving PDEs on Unseen Domainscited by 37
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Citing papers per year: 2023: 3.
3 papers.
- 2023Symbolic Regression for PDEs using Pruned Differentiable Programscited by 7
- 2023HyperLoRA for PDEscited by 4
- 2023Redefining Super-Resolution: Fine-mesh PDE predictions without classical simulationscited by 1
- 2023Redefining Super-Resolution: Fine-mesh PDE predictions without classical simulations16 Nov 2023
- 2023HyperLoRA for PDEs18 Aug 2023
- 2023Symbolic Regression for PDEs using Pruned Differentiable Programs13 Mar 2023