Constraint-Aware Neural Networks for Riemann Problems

Understand

Neural networks are increasingly used in complex (data-driven) simulations as surrogates or for accelerating the computation of classical surrogates.

• In many applications physical constraints, such as mass or energy conservation, must be satisfied to obtain reliable results.
• However, standard machine learning algorithms are generally not tailored to respect such constraints.
• We propose two different strategies to generate constraint-aware neural networks.