PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation
in Ocean Modeling

PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation in Ocean Modeling

5 May 2021

Björn Lütjens

Catherine H. Crawford

Mark S. Veillette

Papers citing "PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation in Ocean Modeling"

6 / 6 papers shown

Title
Metamizer: a versatile neural optimizer for fast and accurate physics simulations Nils Wandel Stefan Schulz Reinhard Klein PINN AI4CE 53 1 0 10 Oct 2024
Learning Reduced-Order Models for Cardiovascular Simulations with Graph Neural Networks Luca Pegolotti Martin R. Pfaller Natalia L. Rubio Ke Ding Rita Brugarolas Brufau Eric F. Darve Alison L. Marsden AI4CE 53 31 0 13 Mar 2023
Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers Björn Lütjens Catherine H. Crawford C. Watson C. Hill Dava Newman AI4CE 19 9 0 23 Jul 2022
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling Arka Daw Jie Bu Sizhuang He P. Perdikaris Anuj Karpatne AI4CE 36 46 0 05 Jul 2022
When happy accidents spark creativity: Bringing collaborative speculation to life with generative AI Ziv Epstein Hope Schroeder Dava Newman 19 23 0 01 Jun 2022
Towards Optimally Weighted Physics-Informed Neural Networks in Ocean Modelling T. Wolff Hugo Carrillo Lincopi Luis Martí Nayat Sánchez-Pi PINN AI4CE 16 12 0 16 Jun 2021