Pre-training physics-informed neural network with mixed sampling and its application in high-dimensional systems (Q6130985)

scientific article; zbMATH DE number 7827314

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English	Pre-training physics-informed neural network with mixed sampling and its application in high-dimensional systems	scientific article; zbMATH DE number 7827314

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instance of

scholarly article

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title

Pre-training physics-informed neural network with mixed sampling and its application in high-dimensional systems (English)

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DOI

10.1007/s11424-024-3321-y

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Journal of Systems Science and Complexity

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publication date

3 April 2024

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Mathematics Subject Classification ID

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high-dimensional systems

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mixed sampling

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nonlinear wave

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pre-training physics-informed neural network

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MaRDI profile type

MaRDI publication profile

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full work available at URL

https://doi.org/10.1007/s11424-024-3321-y

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OpenAlex ID

W4391260377

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cites work

Human-level concept learning through probabilistic program induction

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Q5270493

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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

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Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks

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A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

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Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks

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When and why PINNs fail to train: a neural tangent kernel perspective

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Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations

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Parallel physics-informed neural networks via domain decomposition

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fPINNs: Fractional Physics-Informed Neural Networks

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A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations

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Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

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On the soliton resolution and the asymptotic stability of \(N\)-soliton solution for the Wadati-Konno-Ichikawa equation with finite density initial data in space-time solitonic regions

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Soliton resolution for the Wadati-Konno-Ichikawa equation with weighted Sobolev initial data

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Soliton resolution for the complex short pulse equation with weighted Sobolev initial data in space-time solitonic regions

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Predicting the dynamic process and model parameters of the vector optical solitons in birefringent fibers \textit{via} the modified PINN

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Data-driven rogue waves and parameter discovery in the defocusing nonlinear Schrödinger equation with a potential using the PINN deep learning

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The nonlinear wave solutions and parameters discovery of the Lakshmanan-Porsezian-Daniel based on deep learning

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Data-driven vector soliton solutions of coupled nonlinear Schrödinger equation using a deep learning algorithm

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Data-driven soliton mappings for integrable fractional nonlinear wave equations via deep learning with Fourier neural operator

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Data-driven peakon and periodic peakon solutions and parameter discovery of some nonlinear dispersive equations via deep learning

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A two-stage physics-informed neural network method based on conserved quantities and applications in localized wave solutions

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Physics-informed neural network methods based on Miura transformations and discovery of new localized wave solutions

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Q4558473

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On the limited memory BFGS method for large scale optimization

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Large Sample Properties of Simulations Using Latin Hypercube Sampling

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On two-dimensional packets of capillary-gravity waves

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On the evolution of packets of water waves

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Exact solutions of the Wick-type stochastic Kadomtsev-Petviashvili equations

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Rogue waves for a variable-coefficient Kadomtsev-Petviashvili equation in fluid mechanics

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Pfaffianization of the generalized variable-coefficient Kadomtsev-Petviashvili equation

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Wikidata QID

Q129284087

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Sitelinks

Mathematics(1 entry)

mardi Publication:6130985