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Found 4 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.
Finding the Optimum Design of Large Gas Engines Prechambers Using CFD and Bayesian Optimization
The turbulent jet ignition concept using prechambers is a promising solution to achieve stable combustion at lean conditions in large gas engines, leading to high efficiency at low emission levels.
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.
