no code implementations • 3 Sep 2024 • Filippo Aglietti, Francesco Della Santa, Andrea Piano, Virginia Aglietti
We propose Gradient Informed Neural Networks (GradINNs), a methodology inspired by Physics Informed Neural Networks (PINNs) that can be used to efficiently approximate a wide range of physical systems for which the underlying governing equations are completely unknown or cannot be defined, a condition that is often met in complex engineering problems.
