Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology

13 August 2024

Papers citing "Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology"

6 / 6 papers shown

Title
Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble Zongren Zou Zhicheng Wang George Karniadakis PINN AI4CE 70 2 0 08 Mar 2025
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 PINN 20 1 0 18 Mar 2024
AI-Lorenz: A physics-data-driven framework for black-box and gray-box identification of chaotic systems with symbolic regression Mario De Florio Ioannis G. Kevrekidis George Karniadakis 41 16 0 21 Dec 2023
A Method for Computing Inverse Parametric PDE Problems with Random-Weight Neural Networks S. Dong Yiran Wang 29 20 0 09 Oct 2022
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 183 759 0 13 Mar 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,661 0 05 Dec 2016