This repository includes code for evaluating the effectiveness of B-PINNs in simulating various simple problems using data with noise.
-
Spring Mass Damper System
-
1D Steady State Heat Equation
Different Measurement Noise Levels used in the training data.
-
No Noise
-
Low Noise (S: 0.001,
$\omega_\text{cut,in}$ : 3.14 rad/s,$\omega_\text{cut,off}$ : 50 rad/s) -
High Noise (S: 0.01,
$\omega_\text{cut,in}$ : 3.14 rad/s,$\omega_\text{cut,off}$ : 50 rad/s)
In this repo, we integrate data, physics, and uncertainties by combining neural networks, physics informed modeling, and Bayesian inference to improve the predictive potential of traditional neural network models.