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Found 5 papers, 0 papers with code
Error Analysis of Physics-Informed Neural Networks for Approximating Dynamic PDEs of Second Order in Time
Our analyses show that, with feed-forward neural networks having two hidden layers and the $\tanh$ activation function, the PINN approximation errors for the solution field, its time derivative and its gradient field can be effectively bounded by the training loss and the number of training data points (quadrature points).
A posteriori error analysis of hybrid high-order method for the Stokes problem
We present a residual-based a posteriori error estimator for the hybrid high-order (HHO) method for the Stokes model problem.
Numerical Analysis Numerical Analysis
PASSAT at Future Neutrino Experiments: Hybrid Beam-Dump-Helioscope Facilities to Probe Light Axion-Like Particles
There are broadly three channels to probe axion-like particles (ALPs) produced in the laboratory: through their subsequent decay to Standard Model (SM) particles, their scattering with SM particles, or their subsequent conversion to photons.
High Energy Physics - Phenomenology High Energy Physics - Experiment
Prospects of gravitational waves in the minimal left-right symmetric model
The left-right symmetric model (LRSM) is a well-motivated framework to restore parity and implement seesaw mechanisms for the tiny neutrino masses at or above the TeV-scale, and has a very rich phenomenology at both the high-energy and high-precision frontiers.
High Energy Physics - Phenomenology
Deep Reinforcement Learning-Based Dynamic Resource Management for Mobile Edge Computing in Industrial Internet of Things
In order to minimize the long-term average delay of the tasks, theoriginal problem is transformed into a Markov decision process (MDP).
