4 papers · 1 filter
Dirac-Interconnected Neural Elements: Discovering Modularity in Physical Systems Without Reduction
Reiho Li, Razmik Arman Khosrovian, Takaharu Yaguchi +2
Deep learning has shown remarkable success in the data-driven modeling of dynamical systems. Much of its success is attributed not to the flexibility of neural networks but to indu…
PINNs Failure Modes are Overfitting
Nigel T. Andersen, Takashi Matsubara
Physics-Informed Neural Networks (PINNs) are a common class of machine learning-based partial differential equation (PDE) solvers which train a network to represent a solution by m…
Learning Hamiltonian Density Using DeepONet
Baige Xu, Yusuke Tanaka, Takashi Matsubara +1
In recent years, deep learning for modeling physical phenomena which can be described by partial differential equations (PDEs) have received significant attention. For example, for…
Poisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems across Domains
Razmik Arman Khosrovian, Takaharu Yaguchi, Hiroaki Yoshimura +1
Deep learning has achieved great success in modeling dynamical systems, providing data-driven simulators to predict complex phenomena, even without known governing equations. Howev…