Energy-dissipative evolutionary deep operator neural networks
From MaRDI portal
Publication:6187616
DOI10.1016/J.JCP.2023.112638arXiv2306.06281OpenAlexW4388907411MaRDI QIDQ6187616
No author found.
Publication date: 31 January 2024
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2306.06281
deep learningparametric equationevolutionary neural networksscalar auxiliary variableoperator learningenergy dissipative
Artificial intelligence (68Txx) Numerical methods for partial differential equations, boundary value problems (65Nxx) Miscellaneous topics in partial differential equations (35Rxx)
Cites Work
- A rapidly converging phase field model
- The scalar auxiliary variable (SAV) approach for gradient flows
- Multilayer feedforward networks are universal approximators
- A physics-informed operator regression framework for extracting data-driven continuum models
- Data-driven deep learning of partial differential equations in modal space
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
- ConvPDE-UQ: convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains
- The Random Feature Model for Input-Output Maps between Banach Spaces
- Convergence of the phase field model to its sharp interface limits
- Deep learning in high dimension: Neural network expression rates for generalized polynomial chaos expansions in UQ
- A diffuse-interface method for simulating two-phase flows of complex fluids
- DIFFUSE-INTERFACE METHODS IN FLUID MECHANICS
- TWO-PHASE BINARY FLUIDS AND IMMISCIBLE FLUIDS DESCRIBED BY AN ORDER PARAMETER
- Solving parametric PDE problems with artificial neural networks
- DEEP LEARNING OF PARAMETERIZED EQUATIONS WITH APPLICATIONS TO UNCERTAINTY QUANTIFICATION
- Free Energy of a Nonuniform System. I. Interfacial Free Energy
- Approximation by superpositions of a sigmoidal function
This page was built for publication: Energy-dissipative evolutionary deep operator neural networks