Results 41 to 50 of about 2,349 (245)
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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Nuclear Engineering and Technology, 2023 The governing equations of atmospheric dispersion most often taking the form of a second-order partial differential equation (PDE). Currently, typical computational codes for predicting atmospheric dispersion use the Gaussian plume model that is an ...
Gibeom Kim, Gyunyoung Heo
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Physics-Informed Neural Networks (PINNs) for MHD:Results and Issues
, 2023 Presentation at DASDH 2023 meeting at JHU APL on 2023-10 ...
Winter, Eric, Weigel, Bob
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Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation
, 2023 Full version of the paper accepted for the SDM23 conference; Yan Li and Mingzhou Yang contributed equally to this ...
Yan Li 0049 +6 more
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Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs
Advances in Computational Mathematics, 2022 AbstractPhysics-informed neural networks approximate solutions of PDEs by minimizing pointwise residuals. We derive rigorous bounds on the error, incurred by PINNs in approximating the solutions of a large class of linear parabolic PDEs, namely Kolmogorov equations that include the heat equation and Black-Scholes equation of option pricing, as examples.
Tim De Ryck, Siddhartha Mishra
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Applying physics informed neural network for flow data assimilation
, 2020 Data assimilation (DA) refers to methodologies which combine data and underlying governing equations to provide an estimation of a complex system. Physics informed neural network (PINN) provides an innovative machine learning technique for solving and ...
Bai, X., Wang, Y., Zhang, W.
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Sensors, 2023 This paper proposes a physics-informed neural network (PINN) for predicting the early-age time-dependent behaviors of prestressed concrete beams. The PINN utilizes deep neural networks to learn the time-dependent coupling among the effective prestress ...
Hyun-Woo Park, Jin-Ho Hwang
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E-PINNs: Epistemic Physics-Informed Neural Networks
CoRRPhysics-informed neural networks (PINNs) have demonstrated promise as a framework for solving forward and inverse problems involving partial differential equations. Despite recent progress in the field, it remains challenging to quantify uncertainty in these networks.
Ashish S. Nair +4 more
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Accelerated Training of Physics-Informed Neural Networks (PINNs) Using Meshless Discretizations
Advances in Neural Information Processing Systems 35, 2022 We present a new technique for the accelerated training of physics-informed neural networks (PINNs): discretely-trained PINNs (DT-PINNs). The repeated computation of partial derivative terms in the PINN loss functions via automatic differentiation during training is known to be computationally expensive, especially for higher-order derivatives.
Ramansh Sharma, Varun Shankar
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PINN-MG: a physics-informed neural network for mesh generation
Communications in Information and SystemsAccepted by Chinagraph2024 and recommended for publication in Communications in Information and ...
Min Wang +4 more
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