Learning the Dynamics for Unknown Hyperbolic Conservation Laws Using Deep Neural Networks
From MaRDI portal
Publication:6195013
DOI10.1137/22m1537333MaRDI QIDQ6195013
No author found.
Publication date: 12 March 2024
Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)
Artificial neural networks and deep learning (68T07) Numerical optimization and variational techniques (65K10) Shocks and singularities for hyperbolic equations (35L67) Hyperbolic conservation laws (35L65) Stability and convergence of numerical methods for initial value and initial-boundary value problems involving PDEs (65M12) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65M99) Neural nets and related approaches to inference from stochastic processes (62M45)
Cites Work
- Unnamed Item
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- Efficient implementation of essentially nonoscillatory shock-capturing schemes
- Data-driven deep learning of partial differential equations in modal space
- A neural network based shock detection and localization approach for discontinuous Galerkin methods
- A data-driven physics-informed finite-volume scheme for nonclassical undercompressive shocks
- On generalized residual network for deep learning of unknown dynamical systems
- Deep neural network modeling of unknown partial differential equations in nodal space
- Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems
- Data driven governing equations approximation using deep neural networks
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Total variation diminishing Runge-Kutta schemes
- Finite Volume Methods for Hyperbolic Problems
- Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
- Extracting Sparse High-Dimensional Dynamics from Limited Data
- Numerical Methods for Conservation Laws
- Robust data-driven discovery of governing physical laws with error bars
- Learning partial differential equations via data discovery and sparse optimization
- Tikhonov Regularization and Total Least Squares
- Data-Driven Learning of Nonautonomous Systems
- DEEP LEARNING OF PARAMETERIZED EQUATIONS WITH APPLICATIONS TO UNCERTAINTY QUANTIFICATION
- Discrete Inverse Problems
- Transport, Collective Motion, and Brownian Motion