Pinnsformer: A transformer-based framework for physics-informed neural networks

Z Zhao, X Ding, BA Prakash - arXiv preprint arXiv:2307.11833, 2023 - arxiv.org
Physics-Informed Neural Networks (PINNs) have emerged as a promising deep learning
framework for approximating numerical solutions to partial differential equations (PDEs) …

Learning thermoacoustic interactions in combustors using a physics-informed neural network

S Mariappan, K Nath, GE Karniadakis - Engineering Applications of …, 2024 - Elsevier
Many gas turbine and rocket engines exhibit unwanted combustion instability at the
experimental testing phase. Instability leads to large amplitude pressure oscillations and …

Understanding the Difficulty of Solving Cauchy Problems with PINNs

T Wang, B Zhao, S Gao, R Yu - arXiv preprint arXiv:2405.02561, 2024 - arxiv.org
Physics-Informed Neural Networks (PINNs) have gained popularity in scientific computing in
recent years. However, they often fail to achieve the same level of accuracy as classical …