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Found 5 papers, 3 papers with code
A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains
The proposed operator learning framework takes a temperature field at the current time step as input and predicts a temperature field at the next time step.
A finite operator learning technique for mapping the elastic properties of microstructures to their mechanical deformations
Our method, inspired by operator learning and the finite element method, demonstrates the ability to train without relying on data from other numerical solvers.
Theory and implementation of inelastic Constitutive Artificial Neural Networks
As the design of the network is not limited to visco-elasticity, our vision is that the iCANN will reveal to us new ways to find the various inelastic phenomena hidden in the data and to understand their interaction.
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.
A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method
Later on, the strong form which has a higher order of derivatives is applied to the spatial gradients of the primary variable as the physical constraint.
