Ajay Dulichand Borkar1, Dipjyoti
Nath1, Sachin Singh Gautam1,*
The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.011404
Abstract In recent years, machine learning (ML) has emerged as a powerful tool for addressing complex problems in the realms of science and engineering. However, the effectiveness of many state-of-the-art ML techniques is hindered by the limited availability of adequate data, leading to issues of robustness and convergence. Consequently, inferences drawn from such models are often based on partial information. In a seminal contribution, Raissi et al. [1] introduced the concept of physics informed neural networks (PINNs), presenting a novel paradigm in the domain of function approximation by artificial neural networks (ANNs). This advancement marks a… More >
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