Results 31 to 40 of about 80,482 (270)
Physics-informed neural networks for solving nonlinear diffusivity and Biot's equations.
PLoS ONE, 2020 This paper presents the potential of applying physics-informed neural networks for solving nonlinear multiphysics problems, which are essential to many fields such as biomedical engineering, earthquake prediction, and underground energy harvesting ...
Teeratorn Kadeethum +2 more
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Frontiers in Materials, 2022 (Artificial) neural networks have become increasingly popular in mechanics and materials sciences to accelerate computations with model order reduction techniques and as universal models for a wide variety of materials. However, the major disadvantage of
Arnd Koeppe +6 more
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Discontinuity Computing Using Physics-Informed Neural Networks
Journal of Scientific Computing, 2022 Simulating discontinuities is a long standing problem especially for shock waves with strong nonlinear feather. Despite being a promising method, the recently developed physics-informed neural network (PINN) is still weak for calculating discontinuities compared with traditional shock-capturing methods.
Li Liu +7 more
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Physics informed neural networks for continuum micromechanics [PDF]
Computer Methods in Applied Mechanics and Engineering, 2021 AbstractThe present work proposes a Physics Informed Neural Network (PINN) for solving boundary value problems in continuum micromechanics. The presented technique is therefore an alternative to the finite element method or Fourier transform based methods.
Alexander Henkes +2 more
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Physics-informed neural networks for modeling astrophysical shocks
Machine Learning: Science and Technology, 2023 Physics-informed neural networks (PINNs) are machine learning models that integrate data-based learning with partial differential equations (PDEs).
S P Moschou +5 more
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Physics-Informed Neural Networks in Polymers: A Review. [PDF]
Polymers (Basel)The modeling and simulation of polymer systems present unique challenges due to their intrinsic complexity and multi-scale behavior. Traditional computational methods, while effective, often struggle to balance accuracy with computational efficiency, especially when bridging the atomistic to macroscopic scales.
Malashin I +4 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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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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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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