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Found 2 papers, 1 papers with code
Integration of physics-informed operator learning and finite element method for parametric learning of partial differential equations
We benchmark our methodology against the standard finite element method, demonstrating accurate yet faster predictions using the trained neural network for temperature and flux profiles.
Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains
In this work, we propose applying the mixed formulation to solve multi-physical problems, specifically a stationary thermo-mechanically coupled system of equations.
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