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GNN-based physics solver for time-independent PDEs
Physics-based deep learning frameworks have shown to be effective in accurately modeling the dynamics of complex physical systems with generalization capability across problem inputs.
Robust Topology Optimization Using Variational Autoencoders
Robust topology optimization (RTO) also incorporates the effect of input uncertainties and produces a design with the best average performance of the structure while reducing the response sensitivity to input uncertainties.
Efficient training of physics-informed neural networks via importance sampling
This importance sampling approach is straightforward and easy to implement in the existing PINN codes, and also does not introduce any new hyperparameter to calibrate.
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