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Found 5 papers, 1 papers with code
Approximating Families of Sharp Solutions to Fisher's Equation with Physics-Informed Neural Networks
Furthermore, a specific network architecture is studied which is tailored for solutions in the form of traveling waves.
Bringing Chemistry to Scale: Loss Weight Adjustment for Multivariate Regression in Deep Learning of Thermochemical Processes
We assess a simple, yet effective loss weight adjustment that outperforms the standard mean-squared error optimization and enables accurate learning of all species mass fractions, even of minor species where the standard optimization completely fails.
On the Role of Fixed Points of Dynamical Systems in Training Physics-Informed Neural Networks
This paper empirically studies commonly observed training difficulties of Physics-Informed Neural Networks (PINNs) on dynamical systems.
On the Pareto Front of Physics-Informed Neural Networks
We use the diffusion equation and Navier-Stokes equations in various test environments to analyze the effects of system parameters on the shape of the Pareto front.
Importance of feature engineering and database selection in a machine learning model: A case study on carbon crystal structures
In this work we seek to understand in depth the effect that the choice of features and the properties of the database have on a machine learning application.
