Cover Image

A Greedy PDE Router for Blending Neural Operators and Classical Methods

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Rayan, Sahana, Patel, Yash, Tewari, Ambuj
Format:	Preprint
Published:	2025
Subjects:	Methodology Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning to Partially Defer for Sequences
by: Rayan, Sahana, et al.
Published: (2025)

Conformal Robust Control of Linear Systems
by: Patel, Yash, et al.
Published: (2024)

Distribution-Free Robust Predict-Then-Optimize in Function Spaces
by: Patel, Yash, et al.
Published: (2026)

Conformal Prediction for Ensembles: Improving Efficiency via Score-Based Aggregation
by: Rivera, Eduardo Ochoa, et al.
Published: (2024)

Variational Inference with Coverage Guarantees in Simulation-Based Inference
by: Patel, Yash, et al.
Published: (2023)

Continuum Transformers Perform In-Context Learning by Operator Gradient Descent
by: Mishra, Abhiti, et al.
Published: (2025)

Operator Learning for Schrödinger Equation: Unitarity, Error Bounds, and Time Generalization
by: Patel, Yash, et al.
Published: (2025)

On the Computational Complexity of Private High-dimensional Model Selection
by: Roy, Saptarshi, et al.
Published: (2023)

Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventions
by: Brooks, Marc, et al.
Published: (2025)

Operator Learning: A Statistical Perspective
by: Subedi, Unique, et al.
Published: (2025)

On the Benefits of Active Data Collection in Operator Learning
by: Subedi, Unique, et al.
Published: (2024)

Less Greedy Equivalence Search
by: Ejaz, Adiba, et al.
Published: (2025)

If generative AI is the answer, what is the question?
by: Tewari, Ambuj
Published: (2025)

Is Zero-Shot Super-Resolution Possible in Operator Learning?
by: Subedi, Unique, et al.
Published: (2026)

BayesBlend: Easy Model Blending using Pseudo-Bayesian Model Averaging, Stacking and Hierarchical Stacking in Python
by: Haines, Nathaniel, et al.
Published: (2024)

Controlling Statistical, Discretization, and Truncation Errors in Learning Fourier Linear Operators
by: Subedi, Unique, et al.
Published: (2024)

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

Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
by: Gamella, Juan L., et al.
Published: (2022)

Globally-Optimal Greedy Experiment Selection for Active Sequential Estimation
by: Li, Xiaoou, et al.
Published: (2024)

Online Infinite-Dimensional Regression: Learning Linear Operators
by: Raman, Vinod, et al.
Published: (2023)

ACS: An interactive framework for conformal selection
by: Gui, Yu, et al.
Published: (2025)

Benchmark of Likelihood-Free Inference Methods based on Neural and Optimal Transport Approaches
by: Aka, Samira, et al.
Published: (2026)

A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs
by: Hong, Kihyuk, et al.
Published: (2024)

Robust Bayesian Inference for Measurement Error Misspecification: The Berkson and Classical Cases
by: Dellaporta, Charita, et al.
Published: (2023)

On the Minimax Regret in Online Ranking with Top-k Feedback
by: Zhang, Mingyuan, et al.
Published: (2023)

An Asymptotically Optimal Algorithm for the Convex Hull Membership Problem
by: Qiao, Gang, et al.
Published: (2023)

Integrative Learning of Dynamically Evolving Multiplex Graphs and Nodal Attributes Using Neural Network Gaussian Processes with an Application to Dynamic Terrorism Graphs
by: Rodriguez-Acosta, Jose, et al.
Published: (2026)

A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs
by: Hong, Kihyuk, et al.
Published: (2025)

Bayesian Nonlinear PDE Inference via Gaussian Process Collocation with Application to the Richards Equation
by: Yang, Yumo, et al.
Published: (2025)

Bayesian Inference for PDE-based Inverse Problems using the Optimization of a Discrete Loss
by: Amoudruz, Lucas, et al.
Published: (2025)

Causal Graph Aided Causal Discovery in an Observational Aneurysmal Subarachnoid Hemorrhage Study
by: Berzuini, Carlo, et al.
Published: (2024)

A Primer on the Signature Method in Machine Learning
by: Chevyrev, Ilya, et al.
Published: (2016)

A Bootstrap-based Method for Testing Network Similarity
by: Bhadra, Somnath, et al.
Published: (2019)

On Next-Token Prediction in LLMs: How End Goals Determine the Consistency of Decoding Algorithms
by: Trauger, Jacob, et al.
Published: (2025)

Compute Aligned Training: Optimizing for Test Time Inference
by: Ousherovitch, Adam, et al.
Published: (2026)

A Kolmogorov-Arnold Neural Model for Cascading Extremes
by: de Carvalho, Miguel, et al.
Published: (2025)

A PLS-Integrated LASSO Method with Application in Index Tracking
by: Tang, Shiqin, et al.
Published: (2025)

A Divergence-Based Method for Weighting and Averaging Model Predictions
by: Vassend, Olav Benjamin
Published: (2026)

Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries
by: Chao, Patrick, et al.
Published: (2023)

Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks
by: Sainsbury-Dale, Matthew, et al.
Published: (2023)