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cs.LG2023★ 31 cited
PINN Training using Biobjective Optimization: The Trade-off between Data Loss and Residual Loss
Fabian Heldmann, Sarah Berkhahn, Matthias Ehrhardt +1
Physics informed neural networks (PINNs) have proven to be an efficient tool to represent problems for which measured data are available and for which the dynamics in the data are…
cs.LG2020
Efficient and Sparse Neural Networks by Pruning Weights in a Multiobjective Learning Approach
Malena Reiners, Kathrin Klamroth, Michael Stiglmayr
Overparameterization and overfitting are common concerns when designing and training deep neural networks, that are often counteracted by pruning and regularization strategies. How…