Simple yet effective adaptive activation functions for physics-informed neural networks (Q6660250)

scientific article; zbMATH DE number 7964652

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English	Simple yet effective adaptive activation functions for physics-informed neural networks	scientific article; zbMATH DE number 7964652

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instance of

scholarly article

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title

Simple yet effective adaptive activation functions for physics-informed neural networks (English)

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Computer Physics Communications

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publication date

10 January 2025

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zbMATH Keywords

physics-informed neural networks (PINNs)

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adaptive activation functions

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weighted average function

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\(L_2\)-normalization function

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partial differential equations

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MaRDI profile type

Publication

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cites work

Human-level concept learning through probabilistic program induction

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An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications

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Parametric deep energy approach for elasticity accounting for strain gradient effects

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Scientific machine learning through physics-informed neural networks: where we are and what's next

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A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

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Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

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NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations

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Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks

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A generic physics-informed neural network-based constitutive model for soft biological tissues

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Physics-informed deep learning for three-dimensional transient heat transfer analysis of functionally graded materials

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Physics-informed deep learning for melting heat transfer analysis with model-based transfer learning

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Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

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B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

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Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems

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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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\textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition

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

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Learning Specialized Activation Functions for Physics-Informed Neural Networks

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Multilayer feedforward networks are universal approximators

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Approximation by superpositions of a sigmoidal function

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Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations

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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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Identifiers

Mathematics Subject Classification ID

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10.1016/J.CPC.2024.109428

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Sitelinks

Mathematics(1 entry)

mardi Publication:6660250