Physics-informed deep learning for incompressible laminar flows

Physics-informed deep learning for incompressible laminar flows

24 February 2020

Yang Liu

Papers citing "Physics-informed deep learning for incompressible laminar flows"

16 / 16 papers shown

Title
Reliable and Efficient Inverse Analysis using Physics-Informed Neural Networks with Distance Functions and Adaptive Weight Tuning Shota Deguchi Mitsuteru Asai PINN AI4CE 76 0 0 25 Apr 2025
DGNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Yaohua Zang P. Koutsourelakis AI4CE 52 0 0 10 Feb 2025
Initialization-enhanced Physics-Informed Neural Network with Domain Decomposition (IDPINN) Chenhao Si Ming Yan AI4CE PINN 28 3 0 05 Jun 2024
A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains Yusuke Yamazaki Ali Harandi Mayu Muramatsu A. Viardin Markus Apel T. Brepols Stefanie Reese Shahed Rezaei AI4CE 26 12 0 21 May 2024
SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain Pu Ren Chengping Rao Su Chen Jian-Xun Wang Hao-Lun Sun Yang Liu 16 40 0 25 Oct 2022
Neural Conservation Laws: A Divergence-Free Perspective Jack Richter-Powell Y. Lipman Ricky T. Q. Chen 35 49 0 04 Oct 2022
Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks Daniel Amini E. Haghighat R. Juanes PINN AI4CE 16 32 0 07 Sep 2022
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics Rajat Arora AI4CE 18 10 0 30 Jun 2022
Transfer Learning as a Method to Reproduce High-Fidelity NLTE Opacities in Simulations Michael D. Vander Wal R. McClarren K. Humbird AI4CE 14 4 0 28 May 2022
Investigation of Physics-Informed Deep Learning for the Prediction of Parametric, Three-Dimensional Flow Based on Boundary Data Philipp Heger Markus Full Daniel Hilger N. Hosters AI4CE 14 9 0 17 Mar 2022
Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous media Daniel Amini E. Haghighat R. Juanes PINN AI4CE 19 22 0 03 Mar 2022
Physics-informed neural networks for modeling rate- and temperature-dependent plasticity Rajat Arora P. Kakkar Biswadip Dey Amit Chakraborty PINN AI4CE 25 19 0 20 Jan 2022
Physics informed neural networks for continuum micromechanics Alexander Henkes Henning Wessels R. Mahnken PINN AI4CE 8 139 0 14 Oct 2021
Geometry encoding for numerical simulations Amir Maleki J. Heyse Rishikesh Ranade Haiyang He Priya Kasimbeg Jay Pathak 3DV AI4CE 19 2 0 15 Apr 2021
Physics informed deep learning for computational elastodynamics without labeled data Chengping Rao Hao-Lun Sun Yang Liu PINN AI4CE 13 216 0 10 Jun 2020
Active Training of Physics-Informed Neural Networks to Aggregate and Interpolate Parametric Solutions to the Navier-Stokes Equations Christopher J. Arthurs A. King PINN 27 50 0 02 May 2020