Flow map learning (FML), in conjunction with deep neural networks (DNNs)...
We present a numerical framework for learning unknown stochastic dynamic...
Recent work has focused on data-driven learning of the evolution of unkn...
Recent work has focused on data-driven learning of the evolution of unkn...
We present a data-driven numerical approach for modeling unknown dynamic...
We present a numerical framework for deep neural network (DNN) modeling ...
We present a numerical framework for recovering unknown non-autonomous
d...
We present a general numerical approach for constructing governing equat...
We study the problem of identifying unknown processes embedded in
time-d...
A novel correction algorithm is proposed for multi-class classification
...
We present a general numerical approach for learning unknown dynamical
s...
In this work we propose a numerical framework for uncertainty quantifica...
We present a framework for recovering/approximating unknown time-depende...
We present a numerical approach for approximating unknown Hamiltonian sy...
We present a numerical framework for approximating unknown governing
equ...
We present effective numerical algorithms for locally recovering unknown...
We present an explicit construction for feedforward neural network (FNN)...
For neural networks (NNs) with rectified linear unit (ReLU) or binary
ac...