1.1k citations · 1.2k across the 4 of their papers we have counts for
4 papers
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems
Pengzhan Jin, Zhen Zhang, Aiqing Zhu +2
We propose new symplectic networks (SympNets) for identifying Hamiltonian systems from data based on a composition of linear, activation and gradient modules. In particular, we def…
Physics-informed semantic inpainting: Application to geostatistical modeling
Qiang Zheng, Lingzao Zeng, Zhendan Cao +1
A fundamental problem in geostatistical modeling is to infer the heterogeneous geological field based on limited measurements and some prior spatial statistics. Semantic inpainting…
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
Ameya D. Jagtap, George Em Karniadakis
We employ adaptive activation functions for regression in deep and physics-informed neural networks (PINNs) to approximate smooth and discontinuous functions as well as solutions o…
A Recursive Sparse Grid Collocation Method for Differential Equations with White Noise
Z. Zhang, M. V. Tretyakov, B. Rozovskii +1
We consider a sparse grid collocation method in conjunction with a time discretization of the differential equations for computing expectations of functionals of solutions to diffe…