Generative point sampling strategies for physics-informed neural networks

Physics-Informed Neural Networks (PINNs) have been a groundbreaking approach for solving complex boundary-value systems using Neural Networks. Although PINNs are capable of solving Partial Differential Equations (PDEs) relatively quickly, without having knowledge of the solution, their precision is...

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Vydáno v:	Engineering with computers Ročník 41; číslo 5; s. 3219 - 3239
Hlavní autoři:	Kochliaridis, Vasileios, Dilmperis, Ioannis, Palaskos, Achilleas, Vlahavas, Ioannis
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.10.2025 Springer Nature B.V
Témata:	Boundary conditions CAE) and Design Calculus of Variations and Optimal Control; Optimization Classical Mechanics Computer Science Computer-Aided Engineering (CAD Control Deep learning Efficiency Finite volume method Libraries Machine learning Math. Applications in Chemistry Mathematical and Computational Engineering Neural networks Numerical analysis Optimization algorithms Original Article Partial differential equations Physics R&D Research & development Sampling methods Systems Theory Generative modeling Scientific computing Computational science Scientific machine learning Physics-informed neural networks Differential equations
ISSN:	0177-0667, 1435-5663
On-line přístup:	Získat plný text
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