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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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Applied Sciences, 2023 Numerical methods, such as finite element or finite difference, have been widely used in the past decades for modeling solid mechanics problems by solving partial differential equations (PDEs).
Yawen Deng +5 more
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Physics-Informed Neural Networks for Solving Coupled Stokes–Darcy Equation
Entropy, 2022 In this paper, a grid-free deep learning method based on a physics-informed neural network is proposed for solving coupled Stokes–Darcy equations with Bever–Joseph–Saffman interface conditions.
Ruilong Pu, Xinlong Feng
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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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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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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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Meshfree Variational-Physics-Informed Neural Networks (MF-VPINN): An Adaptive Training Strategy
AlgorithmsIn this paper, we introduce a Meshfree Variational-Physics-Informed Neural Network. It is a Variational-Physics-Informed Neural Network that does not require the generation of the triangulation of the entire domain and that can be trained with an ...
Stefano Berrone, Moreno Pintore
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DiffGrad for Physics-Informed Neural Networks
CoRR20 pages, 14 ...
Jamshaid Ul Rahman, Nimra
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ELS pricing with Physics-informed neural network
, 2023 학위논문(석사)--금융공학과,2023. 2I Introduction 1 II Background 3 1 Black-Scholes Model 3 2 Monte Carlo Simulation 6 (a) Sampling procedure 6 (b) Cholesky decomposition 7 3 Finite Difference Method 8 (a) Operator Splitting Method 8 (b) Tridiagonal matrix ...
강승구
core
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. +5 more
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