Results 21 to 30 of about 8,116 (238)
Physics-Informed Neural Networks for Low Reynolds Number Flows over Cylinder
Energies, 2023 Physics-informed neural network (PINN) architectures are recent developments that can act as surrogate models for fluid dynamics in order to reduce computational costs.
Elijah Hao Wei Ang +2 more
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Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics
, 2021 The recently developed physics-informed neural network (PINN) has achieved success in many science and engineering disciplines by encoding physics laws into the loss functions of the neural network such that the network not only conforms to the ...
Shi, Zhiyu +9 more
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Physics-informed Neural Networks:Recent Advances and Prospects [PDF]
Jisuanji kexue, 2022 Physical-informed neural networks (PINN) are a class of neural networks used to solve supervised learning tasks.They not only try to follow the distribution law of the training data, but also follow the physical laws described by partial diffe-rential ...
LI Ye, CHEN Song-can
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Meta-learning PINN loss functions [PDF]
Journal of Computational Physics, 2022 We propose a meta-learning technique for offline discovery of physics-informed neural network (PINN) loss functions. We extend earlier works on meta-learning, and develop a gradient-based meta-learning algorithm for addressing diverse task distributions based on parametrized partial differential equations (PDEs) that are solved with PINNs. Furthermore,
Apostolos F. Psaros +2 more
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Deathlife: Hip Hop and Thanatological Narrations of Blackness
, 2023 Anthony Pinn examines how hip hop artists challenge white supremacist definitions of Blackness by challenging white distinctions between life and ...
Pinn, Anthony B.
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Failure-Informed Adaptive Sampling for PINNs
SIAM Journal on Scientific Computing, 2023 Physics-informed neural networks (PINNs) have emerged as an effective technique for solving PDEs in a wide range of domains. It is noticed, however, the performance of PINNs can vary dramatically with different sampling procedures. For instance, a fixed set of (prior chosen) training points may fail to capture the effective solution region (especially ...
Zhiwei Gao +2 more
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Predicting Temperature Field for Metal Additive Manufacturing using PINN [PDF]
, 2023 Machine-learning-based methods are gaining traction as an alternative to numerical methods in many engineering applications. Physics-informed neural network (PINN), a self-supervised method, is particularly attractive with its unique capability of ...
Peng, B., Panesar, A.
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MC-Nonlocal-PINNs: Handling Nonlocal Operators in PINNs via Monte Carlo Sampling
Numerical Mathematics: Theory, Methods and Applications, 2023 We propose Monte Carlo Nonlocal physics-informed neural networks (MC-Nonlocal-PINNs), which are a generalization of MC-fPINNs in L. Guo et al. (Comput. Methods Appl. Mech. Eng. 400 (2022), 115523) for solving general nonlocal models such as integral equations and nonlocal PDEs.
Xiaodong Feng, Yue Qian, Wanfang Shen
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Alexandria Engineering Journal, 2023 Partial differential equations (PDEs) are essential mathematical models for describing a wide range of physical phenomena. Numerically, Physic-Informed Neural Networks (PINNs), a variant of artificial neural networks, present a promising method for ...
Danang A. Pratama +4 more
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Transfer learning-enhanced physics informed neural network for accurate melt pool prediction in laser melting [PDF]
Advanced ManufacturingThe profile of the melt pool is essential in selective laser melting (SLM) to control the process quality and avoid defects. Physics informed neural network (PINN) method is proposed to address challenges in various science and engineering problems when ...
Qingyun Zhu, Zhengxin Lu, Yaowu Hu
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