We present a physics-informed machine-learning (PIML) approach for the
a...
We propose a machine-learning approach to model long-term out-of-sample
...
We study the mechanisms of pattern formation for vegetation dynamics in
...
We present a physics-informed machine learning (PIML) scheme for the fee...
We present an Equation/Variable free machine learning (EVFML) framework ...
We address a physics-informed neural network based on the concept of ran...
The effective control of the COVID-19 pandemic is one the most challengi...
We address a three-tier data-driven approach to solve the inverse proble...
We address a three-tier numerical framework based on manifold learning f...
We propose a numerical scheme based on Random Projection Neural Networks...
We address a biophysical network dynamical model to study how the modula...
We address a new numerical scheme based on a class of machine learning
m...
We introduce a new numerical method based on machine learning to approxi...
We construct embedded functional connectivity networks (FCN) from benchm...
We localize the sources of brain activity of children with epilepsy base...
We perform both analytical and numerical bifurcation analysis of a
fores...
We address a numerical methodology for the computation of coarse-grained...