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Found 5 papers, 4 papers with code
Amortized Finite Element Analysis for Fast PDE-Constrained Optimization
Optimizing the parameters of partial differential equations (PDEs), i. e., PDE-constrained optimization (PDE-CO), allows us to model natural systems from observations or perform rational design of structures with complicated mechanical, thermal, or electromagnetic properties.
Learning the nonlinear dynamics of soft mechanical metamaterials with graph networks
We verify the accuracy of our machine learning approach against several representative numerical examples.
Hybrid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification
In the numerical and experimental examples, the effectiveness of adding auxiliary training data and using the pretrained model on training efficiency and prediction accuracy, as well as the ability to identify unknown parameters with partially observed data, are demonstrated.
Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate
We compare our approach to direct optimization of the design using the learned surrogate, and to supervised learning of the synthesis problem.
Learning Composable Energy Surrogates for PDE Order Reduction
We use a neural network to model the stored potential energy in a component given boundary conditions.
