Burgers' equation
Emerging38papers using it
2022first seen
Burgers' equation is a nonlinear partial differential equation used to evaluate the dynamics of viscous fluid flow, particularly in the context of learning embeddings for solution spaces across varying initial conditions and viscosity values.
Papers using Burgers' equation (38)
- Learning Hidden Physics and System Parameters with Deep Operator NetworksAn Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural NetworksPartitioned Hybrid Quantum Fourier Neural Operators for Scientific Quantum Machine LearningDeep Neural Networks as Discrete Dynamical Systems: Implications for Physics-Informed LearningGradient Enhanced Self-Training Physics-Informed Neural Network (gST-PINN) for Solving Nonlinear Partial Differential EquationsHigh precision PINNs in unbounded domains: application to singularity formulation in PDEsLearning embeddings of non-linear PDEs: the Burgers' equationPhysics-Informed Laplace Neural Operator for Solving Partial Differential EquationsSize is Not the Solution: Deformable Convolutions for Effective Physics Aware Deep LearningOperator Learning at Machine PrecisionPO-CKAN:Physics Informed Deep Operator Kolmogorov Arnold Networks with Chunk Rational StructureLearning to Solve Optimization Problems Constrained with Partial Differential EquationsThermodynamically Consistent Latent Dynamics Identification for Parametric SystemsMesh-free sparse identification of nonlinear dynamicsWavelet Diffusion Neural OperatorQuantum Recurrent Neural Networks with Encoder-Decoder for
Time-Dependent Partial Differential EquationsFrom Mesh to Neural Nets: A Multi-Method Evaluation of Physics-Informed
Neural Networks and Galerkin Finite Element Method for Solving Nonlinear
Convection-Reaction-Diffusion EquationsRegime-Aware Time Weighting for Physics-Informed Neural NetworksConstrained or Unconstrained? Neural-Network-Based Equation Discovery
from DataActive-Learning-Driven Surrogate Modeling for Efficient Simulation of
Parametric Nonlinear SystemsFlow reconstruction by multiresolution optimization of a discrete loss
with automatic differentiationImproving physics-informed neural networks with meta-learned
optimizationBinary structured physics-informed neural networks for solving equations
with rapidly changing solutionsInvestigating Guiding Information for Adaptive Collocation Point
Sampling in PINNsPhysics-informed neural networks with residual/gradient-based adaptive
sampling methods for solving PDEs with sharp solutionsEfficient physics-informed neural networks using hash encodingOptimal time sampling in physics-informed neural networksNeural Integral EquationsPhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural
NetworkHyperLoRA for PDEsDensely Multiplied Physics Informed Neural NetworksDiffGrad for Physics-Informed Neural NetworksFB-HyDON: Parameter-Efficient Physics-Informed Operator Learning of Complex PDEs via Hypernetwork and Finite Basis Domain DecompositionEfficient Error Certification for Physics-Informed Neural NetworksParameter Identification for Partial Differential Equations with
Spatiotemporal Varying CoefficientsEnhancing Convergence Speed with Feature-Enforcing Physics-Informed Neural Networks: Utilizing Boundary Conditions as Prior Knowledge for Faster ConvergenceTENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural
Nets Toward Machine PrecisionKAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for
ordinary/partial differential equation discovery of nonlinear dynamic systems