Results 21 to 30 of about 6,454,505 (250)

Physics-Informed Neural Networks for Cardiac Activation Mapping

Frontiers in Physics, 2020
A critical procedure in diagnosing atrial fibrillation is the creation of electro-anatomic activation maps. Current methods generate these mappings from interpolation using a few sparse data points recorded inside the atria; they neither include prior ...
Francisco Sahli Costabal, Francisco Sahli Costabal, Francisco Sahli Costabal, Yibo Yang, Paris Perdikaris, Daniel E. Hurtado, Daniel E. Hurtado, Daniel E. Hurtado, Ellen Kuhl   +8 more
doaj   +1 more source

Physics-informed neural networks for hydraulic transient analysis in pipeline systems

, 2022
Physics-informed neural networks for hydraulic transient analysis in pipeline ...
J Ye (8430171), W Zeng (10287539), NC Do (14570198), M Lambert (10899522)   +3 more
core  

Recent Developments in Artificial Intelligence in Oceanography

Ocean-Land-Atmosphere Research, 2022
With the availability of petabytes of oceanographic observations and numerical model simulations, artificial intelligence (AI) tools are being increasingly leveraged in a variety of applications.
Changming Dong, Guangjun Xu, Guoqing Han, Brandon J. Bethel, Wenhong Xie, Shuyi Zhou   +5 more
doaj   +1 more source

Physics Informed Neural Networks for Parametrized Partial Differential Equations

, 2023
Physics Informed Neural Networks for Parametrized Partial Differential ...
Prieto Ruiz, Victor Scott
core  

Harnessing the Potential of Physics-Informed Neural Networks (PINNs) in Vortex-Induced Vibration Research

, 2023
Harnessing the Potential of Physics-Informed Neural Networks (PINNs) in Vortex-Induced Vibration ...
Lee Mathias
core   +1 more source

Physics-informed neural networks for inviscid transonic flows around an airfoil [PDF]

Physics-informed neural networks (PINNs) have gained popularity as a deep-learning-based parametric partial differential equation solver. Especially for engineering applications, this approach is promising because a single neural network (NN) could ...
Wassing, Simon, Bekemeyer, Philipp, Langer, Stefan   +2 more
core   +1 more source

Exploring Vortex-Induced Vibration with Tunable Stiffness and Physics-Informed Neural Networks (PINN)

, 2023
Exploring Vortex-Induced Vibration with Tunable Stiffness and Physics-Informed Neural Networks ...
Kevin Lee
core   +1 more source

A Taxonomic Survey of Physics-Informed Machine Learning

Applied Sciences, 2023
Physics-informed machine learning (PIML) refers to the emerging area of extracting physically relevant solutions to complex multiscale modeling problems lacking sufficient quantity and veracity of data with learning models informed by physically relevant
Joseph Pateras, Pratip Rana, Preetam Ghosh   +2 more
doaj   +1 more source

Microbiome−host proteostasis crosstalk—An emerging perspective on mechanisms and interventions toward healthy longevity

FEBS Letters, EarlyView.
Proteostasis and the gut microbiota play a key role in shaping host physiology. Microbiota‐derived metabolites, vitamins, and RNA modulate host proteostasis. Findings from model systems, including C. elegans, indicate microbes can either stabilize or disrupt host proteostasis.
Abhishek Anil Dubey, Maria Ermolaeva
wiley   +1 more source

Physics informed neural networks for 1D flood routing [PDF]

, 2022
Machine learning methods have been widely and successfully applied in hydrological problems. Most of the methods, such as artificial neural networks, have been focused on estimating hydrological data based on observation over time.
Milašinović, Miloš, Milivojević, Nikola, Ivanović, Miloš, Bojović, Filip, Jovanović, Branka, Prodanović, Dušan, Stojanović, Boban, Krstić, Lazar   +7 more
core   +3 more sources