Image de couverture de livre

Structure-Aware Epistemic Uncertainty Quantification for Neural Operator PDE Surrogates

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Song, Haoze, Li, Zhihao, Deng, Mengyi, Li, Xin, Pan, Duyi, Lai, Zhilu, Wang, Wei
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Harnessing Scale and Physics: A Multi-Graph Neural Operator Framework for PDEs on Arbitrary Geometries
par: Li, Zhihao, et autres
Publié: (2024)

From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators
par: Li, Zhihao, et autres
Publié: (2026)

Redefining Neural Operators in $d+1$ Dimensions for Embedding Evolution
par: Song, Haoze, et autres
Publié: (2025)

Direct Learning of Calibration-Aware Uncertainty for Neural PDE Surrogates
par: Brito, Carlos Stein
Publié: (2026)

M2NO: An Efficient Multi-Resolution Operator Framework for Dynamic Multi-Scale PDE Solvers
par: Li, Zhihao, et autres
Publié: (2024)

From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence
par: Wang, Tianyang, et autres
Publié: (2025)

Can We Formally Verify Neural PDE Surrogates? SMT Compilation of Small Fourier Neural Operators
par: Baheri, Ali, et autres
Publié: (2026)

ESI: Epistemic Uncertainty Quantification via Semantic-preserving Intervention for Large Language Models
par: Li, Mingda, et autres
Publié: (2025)

On the Rate of Convergence of Kolmogorov-Arnold Network Regression Estimators
par: Liu, Wei, et autres
Publié: (2025)

HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Model
par: Van Katwyk, Peter, et autres
Publié: (2025)

Quantile-Free Uncertainty Quantification in Graph Neural Networks
par: park, Soyoung, et autres
Publié: (2026)

Surrogate Modeling for Neutron Transport: A Neural Operator Approach
par: Sahadath, Md Hossain, et autres
Publié: (2026)

On the Epistemic Uncertainty of Overparametrized Neural Networks
par: Rügamer, David
Publié: (2026)

From Uniform to Adaptive: General Skip-Block Mechanisms for Efficient PDE Neural Operators
par: Liu, Lei, et autres
Publié: (2025)

Conformalized Neural Networks for Federated Uncertainty Quantification under Dual Heterogeneity
par: Nguyen, Quang-Huy, et autres
Publié: (2026)

Trustworthy Summarization via Uncertainty Quantification and Risk Awareness in Large Language Models
par: Pan, Shuaidong, et autres
Publié: (2025)

Fair Uncertainty Quantification for Depression Prediction
par: Li, Yonghong, et autres
Publié: (2025)

Credal Concept Bottleneck Models: Structural Separation of Epistemic and Aleatoric Uncertainty
par: Mukherjee, Tanmoy, et autres
Publié: (2026)

Uncertainty Quantification for Gradient-based Explanations in Neural Networks
par: Mulye, Mihir, et autres
Publié: (2024)

DiffusionRollout: Uncertainty-Aware Rollout Planning in Long-Horizon PDE Solving
par: Yoo, Seungwoo, et autres
Publié: (2026)

Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators
par: Kobayashi, Kazuma, et autres
Publié: (2025)

CATO: Charted Attention for Neural PDE Operators
par: Cheng, Chun-Wun, et autres
Publié: (2026)

Quantifying Epistemic Uncertainty in Diffusion Models
par: Gupta, Aditi, et autres
Publié: (2026)

Epistemic Uncertainty for Test-Time Discovery
par: Riaz, Kainat, et autres
Publié: (2026)

Operator Feature Neural Network for Symbolic Regression
par: Deng, Yusong, et autres
Publié: (2024)

Developing Distance-Aware, and Evident Uncertainty Quantification in Dynamic Physics-Constrained Neural Networks for Robust Bearing Degradation Estimation
par: Razzaq, Waleed, et autres
Publié: (2025)

Decomposing Epistemic Uncertainty for Causal Decision Making
par: Rahman, Md Musfiqur, et autres
Publié: (2026)

Ensured: Explanations for Decreasing the Epistemic Uncertainty in Predictions
par: Löfström, Helena, et autres
Publié: (2024)

Soft-Label Training Preserves Epistemic Uncertainty
par: Singh, Agamdeep, et autres
Publié: (2025)

OpInf-LLM: Parametric PDE Solving with LLMs via Operator Inference
par: Wang, Zhuoyuan, et autres
Publié: (2026)

DiffHybrid-UQ: Uncertainty Quantification for Differentiable Hybrid Neural Modeling
par: Akhare, Deepak, et autres
Publié: (2023)

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
par: Yu, Dahai, et autres
Publié: (2025)

Gradient-Informed Temporal Sampling Improves Rollout Accuracy in PDE Surrogate Training
par: Wang, Wenshuo, et autres
Publié: (2026)

Gauge-Equivariant Intrinsic Neural Operators for Geometry-Consistent Learning of Elliptic PDE Maps
par: Cheng, Pengcheng
Publié: (2026)

LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process
par: Feng, Xiaodong, et autres
Publié: (2025)

Disentangling Aleatoric and Epistemic Uncertainty in Physics-Informed Neural Networks. Application to Insulation Material Degradation Prognostics
par: Ramirez, Ibai, et autres
Publié: (2026)

Uncertainty Quantification in the Tsetlin Machine
par: Helin, Runar, et autres
Publié: (2025)

Uncertainty Quantification in SVM prediction
par: Anand, Pritam
Publié: (2025)

Generative Latent Neural PDE Solver using Flow Matching
par: Li, Zijie, et autres
Publié: (2025)

Conf-Gen: Conformal Uncertainty Quantification for Generative Models
par: Loaiza-Ganem, Gabriel, et autres
Publié: (2026)