Search Results for author:
Found 4 papers, 0 papers with code
A physics-informed neural network method for the approximation of slow invariant manifolds for the general class of stiff systems of ODEs
We present a physics-informed neural network (PINN) approach for the discovery of slow invariant manifolds (SIMs), for the most general class of fast/slow dynamical systems of ODEs.
Slow Invariant Manifolds of Singularly Perturbed Systems via Physics-Informed Machine Learning
A comparison of the computational costs between symbolic, automatic and numerical approximation of the required derivatives in the learning process is also provided.
Data-driven Control of Agent-based Models: an Equation/Variable-free Machine Learning Approach
We present an Equation/Variable free machine learning (EVFML) framework for the control of the collective dynamics of complex/multiscale systems modelled via microscopic/agent-based simulators.
Parsimonious Physics-Informed Random Projection Neural Networks for Initial-Value Problems of ODEs and index-1 DAEs
The unknown weights between the hidden and output layer are computed by Newton's iterations, using the Moore-Penrose pseudoinverse for low to medium, and sparse QR decomposition with regularization for medium to large scale systems.
