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Journal of Computational Physics
Scopus
ISSN 0021-9991 · Elsevier BV · 16 artikel di indeks
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Artikel Jurnal Teks lengkap
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
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Artikel Jurnal
A-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations
Physics informed neural networks (PINNs) are a novel deep learning paradigm primed for solving forward and inverse problems of nonlinear partial differential equations (PDEs). By embedding physical information delineated by PDEs in feedforward neural networks , PINNs are trained as surrogate models…
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When and why PINNs fail to train: A neural tangent kernel perspective
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Artikel Jurnal Teks lengkap
Physics-informed machine learning for reduced-order modeling of nonlinear problems
A reduced basis method based on a physics-informed machine learning framework is developed for efficient reduced-order modeling of parametrized partial differential equations (PDEs). A feedforward neural network is used to approximate the mapping from the time-parameter to the reduced coefficients…
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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems
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Weak adversarial networks for high-dimensional partial differential equations
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Artikel Jurnal Teks lengkap
Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning
In this paper we introduce constitutive artificial neural networks (CANNs), a novel machine learning architecture for data-driven modeling of the mechanical constitutive behavior of materials. CANNs are able to incorporate by their very design information from three different sources, namely…
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B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
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Artikel Jurnal Teks lengkap
Transfer learning based multi-fidelity physics informed deep neural network
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Artikel Jurnal Teks lengkap
Direct shape optimization through deep reinforcement learning
Deep Reinforcement Learning (DRL) has recently spread into a range of domains within physics and engineering, with multiple remarkable achievements. Still, much remains to be explored before the capabilities of these methods are well understood. In this paper, we present the first application of…