Search Results for author:
Found 5 papers, 3 papers with code
The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory
However, for a majority of deep learning approaches based on DANNs, the kernel structure of neural signal processing remains the same, where the node response is encoded as a linear superposition of neural activity, while the non-linearity is triggered by the activation functions.
Probabilistic Regular Tree Priors for Scientific Symbolic Reasoning
Symbolic Regression (SR) allows for the discovery of scientific equations from data.
A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms
Polynomial chaos expansion (PCE) is a versatile tool widely used in uncertainty quantification and machine learning, but its successful application depends strongly on the accuracy and reliability of the resulting PCE-based response surface.
Composing Partial Differential Equations with Physics-Aware Neural Networks
We introduce a compositional physics-aware FInite volume Neural Network (FINN) for learning spatiotemporal advection-diffusion processes.
Finite Volume Neural Network: Modeling Subsurface Contaminant Transport
To tackle this issue, we introduce a new approach called the Finite Volume Neural Network (FINN).
