EINNs: Epidemiologically-informed Neural Networks

21 February 2022

Papers citing "EINNs: Epidemiologically-informed Neural Networks"

8 / 8 papers shown

Title
Physics-informed deep learning for infectious disease forecasting Y. Qian Éric Marty Avranil Basu Avranil Basu Eamon B. O'Dea Xianqiao Wang Spencer Fox Pejman Rohani John M. Drake He Li PINN AI4CE 76 2 0 16 Jan 2025
Utilising physics-guided deep learning to overcome data scarcity Jinshuai Bai Laith Alzubaidi Qingxia Wang E. Kuhl Bennamoun Yuantong T. Gu PINN AI4CE 14 3 0 24 Nov 2022
Differentiable Agent-based Epidemiology Ayush Chopra Alexander Rodríguez J. Subramanian Arnau Quera-Bofarull Balaji Krishnamurthy B. Prakash Ramesh Raskar AI4CE 16 19 0 20 Jul 2022
Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility information Padmaksha Roy Shailik Sarkar Subhodip Biswas Fanglan Chen Zhiqian Chen Naren Ramakrishnan Chang-Tien Lu DiffM 19 8 0 09 Nov 2021
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting Harshavardhan Kamarthi Lingkai Kong Alexander Rodríguez Chao Zhang B. Prakash AI4TS 33 17 0 15 Sep 2021
Spline-PINN: Approaching PDEs without Data using Fast, Physics-Informed Hermite-Spline CNNs Nils Wandel Michael Weinmann Michael Neidlin Reinhard Klein AI4CE 50 58 0 15 Sep 2021
Physics-based Deep Learning Nils Thuerey Philipp Holl P. Holl Patrick Schnell Felix Trost Kiwon Um P. Schnell F. Trost PINN AI4CE 48 89 0 11 Sep 2021
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 170 755 0 13 Mar 2020