We introduce a novel hybrid methodology that combines classical finite element methods (FEM) with neural networks to create a well-performing and generalizable surrogate model for forward and inverse problems....
The landslide surge is a common secondary disaster of reservoir bank landslides, which can cause more serious damage than the landslide itself in many cases. With the development of large-scale scientific and ...
The authors have developed a novel physics-based nonlinear autoregressive exogeneous neural network model architecture for flight modelling across the entire flight envelope, called FlyNet. When using traditional...
In recent times, artificial neural networks (ANNs) have become the popular choice of model for researchers while performing regression analysis between inputs and output. However; in scientific and engineering...
The behavior of many physical systems is described by means of differential equations. These equations are usually derived from balance principles and certain modelling assumptions. For realistic situations, t...
Nowadays, in the Scientific Machine Learning (SML) research field, the traditional machine learning (ML) tools and scientific computing approaches are fruitfully intersected for solving problems modelled by Pa...