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math.NA2024★ 12 cited
GoRINNs: Godunov-Riemann Informed Neural Networks for Learning Hyperbolic Conservation Laws
Dimitrios G. Patsatzis, Mario di Bernardo, Lucia Russo +1
We present GoRINNs: numerical analysis-informed (shallow) neural networks for the solution of inverse problems of non-linear systems of conservation laws. GoRINNs is a hybrid/blend…
math.NA2024★ 8 cited
A physics-informed neural network method for the approximation of slow invariant manifolds for the general class of stiff systems of ODEs
Dimitrios G. Patsatzis, Lucia Russo, Constantinos Siettos
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…