Cover Image

Scalable Bayesian Inference for Nonlinear Conservation Laws

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Weiland, Tim, Hennig, Philipp
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Numerical Analysis
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Scaling up Probabilistic PDE Simulators with Structured Volumetric Information
by: Weiland, Tim, et al.
Published: (2024)

Flexible and Efficient Probabilistic PDE Solvers through Gaussian Markov Random Fields
by: Weiland, Tim, et al.
Published: (2025)

Computation-Aware Kalman Filtering and Smoothing
by: Pförtner, Marvin, et al.
Published: (2024)

Sketching Low-Rank Plus Diagonal Matrices
by: Fernandez, Andres, et al.
Published: (2025)

Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
by: Pförtner, Marvin, et al.
Published: (2022)

PDE Generalization of In-Context Operator Networks: A Study on 1D Scalar Nonlinear Conservation Laws
by: Yang, Liu, et al.
Published: (2024)

Approximating Numerical Fluxes Using Fourier Neural Operators for Hyperbolic Conservation Laws
by: Kim, Taeyoung, et al.
Published: (2024)

Weak Physics Informed Neural Networks for Geometry Compatible Hyperbolic Conservation Laws on Manifolds
by: Zhou, Hanfei, et al.
Published: (2025)

Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks
by: Gao, Zhiwei, et al.
Published: (2025)

Parallel-in-Time Probabilistic Numerical ODE Solvers
by: Bosch, Nathanael, et al.
Published: (2023)

Variational Bayesian Inference for Tensor Robust Principal Component Analysis
by: Wang, Chao, et al.
Published: (2024)

Stable Derivative Free Gaussian Mixture Variational Inference for Bayesian Inverse Problems
by: Che, Baojun, et al.
Published: (2025)

A Structure-Preserving Graph Neural Solver for Parametric Hyperbolic Conservation Laws
by: Jiang, Jiamin, et al.
Published: (2026)

Nonlinear Dimensionality Reduction Techniques for Bayesian Optimization
by: Long, Luo, et al.
Published: (2025)

SPIKE: Stable Physics-Informed Kernel Evolution Method for Solving Hyperbolic Conservation Laws
by: Su, Hua, et al.
Published: (2025)

Gaussian Processes and Reproducing Kernels: Connections and Equivalences
by: Kanagawa, Motonobu, et al.
Published: (2025)

Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations
by: Li, Zhaohui, et al.
Published: (2022)

LAMP: Look-Ahead Mixed-Precision Inference of Large Language Models
by: Budzinskiy, Stanislav, et al.
Published: (2026)

Scaling Laws and Pathologies of Single-Layer PINNs: Network Width and PDE Nonlinearity
by: Chaudhry, Faris
Published: (2026)

Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws
by: Liu, Ning, et al.
Published: (2023)

Nonlinear model reduction for operator learning
by: Eivazi, Hamidreza, et al.
Published: (2024)

Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows
by: Chen, Yifan, et al.
Published: (2024)

Stochastic Dimension Implicit Functional Projections for Exact Integral Conservation in High-Dimensional PINNs
by: Liang, Zhangyong
Published: (2026)

Integrating Artificial Intelligence, Physics, and Internet of Things: A Framework for Cultural Heritage Conservation
by: Valentino, Carmine, et al.
Published: (2026)

Nonlocality and Nonlinearity Implies Universality in Operator Learning
by: Lanthaler, Samuel, et al.
Published: (2023)

Determinant Estimation under Memory Constraints and Neural Scaling Laws
by: Ameli, Siavash, et al.
Published: (2025)

Finite Expression Methods for Discovering Physical Laws from Data
by: Jiang, Zhongyi, et al.
Published: (2023)

Decentralized Neural Networks for Robust and Scalable Eigenvalue Computation
by: Katende, Ronald
Published: (2024)

Bayesian Quadrature: Gaussian Processes for Integration
by: Mahsereci, Maren, et al.
Published: (2026)

Quantitative Approximation for Neural Operators in Nonlinear Parabolic Equations
by: Furuya, Takashi, et al.
Published: (2024)

Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning
by: Chen, Jiale, et al.
Published: (2024)

Unexpected Improvements to Expected Improvement for Bayesian Optimization
by: Ament, Sebastian, et al.
Published: (2023)

Understanding In-Context Learning for Nonlinear Regression with Transformers: Attention as Featurizer
by: Hsu, Alexander, et al.
Published: (2026)

One-Shot Transfer Learning for Nonlinear PDEs with Perturbative PINNs
by: Auroy, Samuel, et al.
Published: (2025)

Machine Learning-Based Nonlinear Nudging for Chaotic Dynamical Systems
by: Oh, Jaemin, et al.
Published: (2025)

Long-time Integration of Nonlinear Wave Equations with Neural Operators
by: Lei, Guanhang, et al.
Published: (2024)

TGPT-PINN: Nonlinear model reduction with transformed GPT-PINNs
by: Chen, Yanlai, et al.
Published: (2024)

Solving Nonlinear PDEs with Sparse Radial Basis Function Networks
by: Shao, Zihan, et al.
Published: (2025)

Latent Space Inference via Paired Autoencoders
by: Hart, Emma, et al.
Published: (2026)

FLUID: Flow-based Unified Inference for Dynamics
by: Cui, Tiangang, et al.
Published: (2026)