Results 31 to 40 of about 18,341 (302)
Physics informed neural networks for triple deck [PDF]
Aircraft Engineering and Aerospace Technology, 2022 Purpose This paper aims to introduce physics-informed neural networks (PINN) applied to the two-dimensional steady-state laminar Navier–Stokes equations over a flat plate with roughness elements and specified local heating. The method bridges the gap between asymptotics theory and three-dimensional turbulent flow analyses, characterized by high costs ...
Abderrahmane, Belkallouche +3 more
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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 +8 more
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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 +5 more
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Physics Informed Neural Network for Option Pricing
CoRR, 2023 We apply a physics-informed deep-learning approach the PINN approach to the Black-Scholes equation for pricing American and European options. We test our approach on both simulated as well as real market data, compare it to analytical/numerical benchmarks.
Ashish Dhiman 0002, Yibei Hu
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Bayesian Reasoning for Physics Informed Neural Networks
CoRR, 2023 21 pages, 12 figures, re-edit the description of the Bayesian framework, some of the content moved to Appendix.
Krzysztof M. Graczyk, Kornel Witkowski
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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 +2 more
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Complex Physics-Informed Neural Network
Journal of Computational PhysicsWe propose compleX-PINN, a novel physics-informed neural network (PINN) architecture incorporating a learnable activation function inspired by the Cauchy integral theorem. By optimizing the activation parameters, compleX-PINN achieves high accuracy with just a single hidden layer.
Chenhao Si +3 more
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IDRLnet: A Physics-Informed Neural Network Library
CoRR, 2021 Physics Informed Neural Network (PINN) is a scientific computing framework used to solve both forward and inverse problems modeled by Partial Differential Equations (PDEs). This paper introduces IDRLnet, a Python toolbox for modeling and solving problems through PINN systematically.
Wei Peng 0010 +5 more
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Perception-Informed Neural Networks: Beyond Physics-Informed Neural Networks
CoRRThis article introduces Perception-Informed Neural Networks (PrINNs), a framework designed to incorporate perception-based information into neural networks, addressing both systems with known and unknown physics laws or differential equations. Moreover, PrINNs extend the concept of Physics-Informed Neural Networks (PINNs) and their variants, offering a
Mehran Mazandarani, Marzieh Najariyan
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DiffGrad for Physics-Informed Neural Networks
CoRR20 pages, 14 ...
Jamshaid Ul Rahman, Nimra
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