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Main Authors:	Bhandari, Diksha, Pidstrigach, Jakiw, Reich, Sebastian
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Numerical Analysis
Online Access:	https://arxiv.org/abs/2309.04742
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Infinite-Dimensional Diffusion Models
by: Pidstrigach, Jakiw, et al.
Published: (2023)

A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks
by: Yu, Yifan, et al.
Published: (2025)

Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach
by: Fang, Zhiwei, et al.
Published: (2023)

GenUQ: Predictive Uncertainty Estimates via Generative Hyper-Networks
by: Yen, Tian Yu, et al.
Published: (2025)

Localized Diffusion Models
by: Gottwald, Georg A., et al.
Published: (2025)

Transformed Physics-Informed Neural Networks for The Convection-Diffusion Equation
by: Guan, Jiajing, et al.
Published: (2024)

Control Consistency Losses for Diffusion Bridges
by: Howard, Samuel, et al.
Published: (2025)

Pychop: Emulating Low-Precision Arithmetic in Numerical Methods and Neural Networks
by: Carson, Erin, et al.
Published: (2025)

eXtended Physics Informed Neural Network Method for Fracture Mechanics Problems
by: Lotfalian, Amin, et al.
Published: (2025)

Affine Tracing: A New Paradigm for Probabilistic Linear Solvers
by: Hegde, Disha, et al.
Published: (2026)

Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks
by: Baptista, Ricardo, et al.
Published: (2025)

Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
by: Jin, Bangti, et al.
Published: (2025)

A Multiple Transferable Neural Network Method with Domain Decomposition for Elliptic Interface Problems
by: Lu, Tianzheng, et al.
Published: (2025)

Data-Parallel Neural Network Training via Nonlinearly Preconditioned Trust-Region Method
by: Alegría, Samuel A. Cruz, et al.
Published: (2025)

Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs
by: Mouli, S. Chandra, et al.
Published: (2024)

A Novel Methodology in Credit Spread Prediction Based on Ensemble Learning and Feature Selection
by: Shao, Yu, et al.
Published: (2024)

N-Adaptive Ritz Method: A Neural Network Enriched Partition of Unity for Boundary Value Problems
by: Baek, Jonghyuk, et al.
Published: (2024)

Data-Augmented Predictive Deep Neural Network: Enhancing the extrapolation capabilities of non-intrusive surrogate models
by: Sun, Shuwen, et al.
Published: (2024)

Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations
by: Zheng, Haoyang, et al.
Published: (2025)

Ensemble Kalman Methods: A Mean Field Perspective
by: Calvello, Edoardo, et al.
Published: (2022)

A Numerical Method for Coupling Parameterized Physics-Informed Neural Networks and FDM for Advanced Thermal-Hydraulic System Simulation
by: Shin, Jeesuk, et al.
Published: (2026)

An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
by: Gonon, Lukas, et al.
Published: (2024)

Universal Approximation of Operators with Transformers and Neural Integral Operators
by: Zappala, Emanuele, et al.
Published: (2024)

Multigrade Neural Network Approximation
by: Zhang, Shijun, et al.
Published: (2026)

Sampling via Gradient Flows in the Space of Probability Measures
by: Chen, Yifan, et al.
Published: (2023)

Revisiting Orbital Minimization Method for Neural Operator Decomposition
by: Ryu, J. Jon, et al.
Published: (2025)

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

Neural Parameter Regression for Explicit Representations of PDE Solution Operators
by: Mundinger, Konrad, et al.
Published: (2024)

Constrained Ensemble Langevin Monte Carlo
by: Ding, Zhiyan, et al.
Published: (2021)

HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
by: Bryutkin, Andrey, et al.
Published: (2024)

Generative Feature Training of Thin 2-Layer Networks
by: Hertrich, Johannes, et al.
Published: (2024)

Preconditioning for Physics-Informed Neural Networks
by: Liu, Songming, et al.
Published: (2024)

THINNs: Thermodynamically Informed Neural Networks
by: Castro, Javier, et al.
Published: (2025)

Blending Neural Operators and Relaxation Methods in PDE Numerical Solvers
by: Zhang, Enrui, et al.
Published: (2022)

Ensemble Kalman filter for uncertainty in human language comprehension
by: Bhandari, Diksha, et al.
Published: (2025)

Approximating Matrix Functions with Deep Neural Networks and Transformers
by: Padmanabhan, Rahul, et al.
Published: (2026)

Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
by: Moya, Christian, et al.
Published: (2024)

Solving and learning advective multiscale Darcian dynamics with the Neural Basis Method
by: Wang, Yuhe, et al.
Published: (2026)

Dual-Balancing for Physics-Informed Neural Networks
by: Zhou, Chenhong, et al.
Published: (2025)

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