Learned Turbulence Modelling with Differentiable Fluid Solvers: Physics-based Loss-functions and Optimisation Horizons

14 February 2022

Papers citing "Learned Turbulence Modelling with Differentiable Fluid Solvers: Physics-based Loss-functions and Optimisation Horizons"

8 / 8 papers shown

Title
A domain decomposition-based autoregressive deep learning model for unsteady and nonlinear partial differential equations Sheel Nidhan Haoliang Jiang Lalit Ghule C. Umphrey Rishikesh Ranade Jay Pathak AI4CE 34 0 0 26 Aug 2024
Beyond Closure Models: Learning Chaotic-Systems via Physics-Informed Neural Operators Chuwei Wang Julius Berner Zongyi Li Di Zhou Jiayun Wang Jane Bae Anima Anandkumar AI4CE 28 1 0 09 Aug 2024
A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty A. Agrawal P. Koutsourelakis AI4CE 11 15 0 05 Jul 2023
Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations Arnulf Jentzen Adrian Riekert Philippe von Wurstemberger 19 1 0 07 Feb 2023
Solving Inverse Physics Problems with Score Matching Benjamin Holzschuh S. Vegetti Nils Thuerey DiffM 11 8 0 24 Jan 2023
Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing Salah A. Faroughi N. Pawar C. Fernandes Maziar Raissi Subasish Das N. Kalantari S. K. Mahjour PINN AI4CE 27 47 0 14 Nov 2022
Physics-based Deep Learning Nils Thuerey Philipp Holl P. Holl Patrick Schnell Felix Trost Kiwon Um P. Schnell F. Trost PINN AI4CE 48 92 0 11 Sep 2021
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 203 2,281 0 18 Oct 2020