Image de couverture de livre

Structure-Preserving Operator Learning: Modeling the Collision Operator of Kinetic Equations

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Lee, Jae Yong, Schotthöfer, Steffen, Xiao, Tianbai, Krumscheid, Sebastian, Frank, Martin
Format:	Preprint
Publié:	2024
Sujets:	Numerical Analysis
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Numerically Informed Convolutional Operator Network with Subproblem Decomposition for Poisson Equations
par: Jung, Kyoungjin, et autres
Publié: (2026)

Numerical Schemes for 3-Wave Kinetic Equations: A Complete Treatment of the Collision Operator
par: Walton, Steven, et autres
Publié: (2024)

Structure-preserving neural networks for the regularized entropy-based closure of the Boltzmann moment system
par: Schotthöfer, Steffen, et autres
Publié: (2024)

Data-Driven Filtering of the Spherical Harmonics Method
par: Plumridge, Benjamin, et autres
Publié: (2025)

Construction of high-order conservative basis-update and Galerkin dynamical low-rank integrators
par: Einkemmer, Lukas, et autres
Publié: (2023)

An Investigation into the Distribution of Ratios of Particle Solver-based Likelihoods
par: Løvbak, Emil, et autres
Publié: (2025)

An Augmented Backward-Corrected Projector Splitting Integrator for Dynamical Low-Rank Training
par: Kusch, Jonas, et autres
Publié: (2025)

Multi-objective optimization for multi-agent injection strategies in subsurface CO$_2$ storage
par: Pettersson, Per, et autres
Publié: (2024)

Pseudo-Differential Neural Operator: Generalized Fourier Neural Operator for Learning Solution Operators of Partial Differential Equations
par: Shin, Jin Young, et autres
Publié: (2022)

Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks
par: Schotthöfer, Steffen, et autres
Publié: (2025)

Structure-Preserving Operator Learning
par: Bouziani, Nacime, et autres
Publié: (2024)

Windowing Regularization Techniques for Unsteady Aerodynamic Shape Optimization
par: Schotthöfer, Steffen, et autres
Publié: (2024)

Numerical Treatment of Non-local Integral Operators in the Framework of Evolutionary Equations
par: Franz, Sebastian, et autres
Publié: (2026)

LegONet: Plug-and-Play Structure-Preserving Neural Operator Blocks for Compositional PDE Learning
par: Zhang, Jiahao, et autres
Publié: (2026)

A Mass Preserving Numerical Scheme for Kinetic Equations that Model Social Phenomena
par: Bahid, Yassin, et autres
Publié: (2025)

Implicit Dynamical Tensor Train Approximation for Kinetic Equations with Stiff Fokker--Planck Collisions
par: Wang, Geshuo, et autres
Publié: (2026)

On a Modified Random Genetic Drift Model: Derivation and a Structure-Preserving Operator-Splitting Discretization
par: Chen, Chi-An, et autres
Publié: (2025)

FourierSpecNet: Neural Collision Operator Approximation Inspired by the Fourier Spectral Method for Solving the Boltzmann Equation
par: Lee, Jae Yong, et autres
Publié: (2025)

Geometry-aware training of factorized layers in tensor Tucker format
par: Zangrando, Emanuele, et autres
Publié: (2023)

Gradient Preserving Operator Inference: Data-Driven Reduced-Order Models for Equations with Gradient Structure
par: Geng, Yuwei, et autres
Publié: (2024)

An immersed boundary method for the discrete velocity model of the Boltzmann equation
par: Ge, Longqing, et autres
Publié: (2025)

Double-loop randomized quasi-Monte Carlo estimator for nested integration
par: Bartuska, Arved, et autres
Publié: (2023)

An Efficient Explicit-Implicit Adaptive Method for Peridynamic Modelling of Quasi-Static Fracture Formation and Evolution
par: Hu, Shiwei, et autres
Publié: (2024)

An Energy Stable Approach for Learning Derivative Operators from Noisy Data for Maxwells Equations
par: Obieke, Victory, et autres
Publié: (2026)

Parallel-in-Time Solution of Allen-Cahn Equations by Integrating Operator Learning into the Parareal Method
par: Geng, Yuwei, et autres
Publié: (2025)

Data-driven Model Reduction for Parameter-Dependent Matrix Equations via Operator Inference
par: Wen, Xuelian, et autres
Publié: (2025)

Structure-Preserving Numerical Methods for Fokker-Planck Equations
par: Bartel, Hanna, et autres
Publié: (2024)

Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation
par: Sun, Jingmin, et autres
Publié: (2024)

Solving Convolution-type Integral Equations using Preconditioned Neural Operators
par: Chan, Raymond, et autres
Publié: (2026)

GeoLoRA: Geometric integration for parameter efficient fine-tuning
par: Schotthöfer, Steffen, et autres
Publié: (2024)

Structure-Preserving Neural Ordinary Differential Equations for Stiff Systems
par: Loya, Allen Alvarez, et autres
Publié: (2025)

Diffeomorphic Neural Operator Learning
par: Taylor, Seth, et autres
Publié: (2025)

NSNO: Neumann Series Neural Operator for Solving Helmholtz Equations in Inhomogeneous Medium
par: Chen, Fukai, et autres
Publié: (2024)

A Geometry-Aware Operator Learning Framework for Interface Problems on Varying Domains
par: Xiao, Shanshan, et autres
Publié: (2026)

A Conservative and Positivity-Preserving Discontinuous Galerkin Method for the Population Balance Equation
par: Xu, Ziyao, et autres
Publié: (2025)

Reliable Uncertainty Quantification for Fiber Orientation in Composite Molding Processes using Multilevel Polynomial Surrogates
par: Salatovic, Stjepan, et autres
Publié: (2024)

Stable Sparse Operator Inference for Nonlinear Structural Dynamics
par: Boef, Pascal den, et autres
Publié: (2024)

Kernel Learning of PDE Solution Operators
par: Hu, Jianyu, et autres
Publié: (2026)

Quantitative Approximation for Neural Operators in Nonlinear Parabolic Equations
par: Furuya, Takashi, et autres
Publié: (2024)

On the Approximation of Operator-Valued Riccati Equations in Hilbert Spaces
par: Cheung, James
Publié: (2023)