Search Results for author:
Found 3 papers, 1 papers with code
PINN Training using Biobjective Optimization: The Trade-off between Data Loss and Residual Loss
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 expected to follow some physical laws.
Event-based MILP models for ride-hailing applications
We consider a static dial-a-ride problem (DARP) where a series of origin-destination requests have to be assigned to routes of a fleet of vehicles.
Optimization and Control
Efficient and Sparse Neural Networks by Pruning Weights in a Multiobjective Learning Approach
We suggest a multiobjective perspective on the training of neural networks by treating its prediction accuracy and the network complexity as two individual objective functions in a biobjective optimization problem.
