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Found 7 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.
"UWBCarGraz" Dataset for Car Occupancy Detection using Ultra-Wideband Radar
Our presented ResNet architecture is able to outperform the VMP algorithm in terms of the area under the receiver operating curve (AUC) at low signal-to-noise ratios (SNRs) for all three activity levels of the target.
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.
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.
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.
Knock Detection in Combustion Engine Time Series Using a Theory-Guided 1D Convolutional Neural Network Approach
In addition, the model proved to classify knocking cycles in unseen engines with increased accuracy of 89% after adapting to their features via training on a small number of exclusively non-knocking cycles.
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.
