Cover Image

Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kumar, Sawan, Nayek, Rajdip, Chakraborty, Souvik
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics
by: Kumar, Sawan, et al.
Published: (2024)

From Local Interactions to Global Operators: Scalable Gaussian Process Operator for Physical Systems
by: Kumar, Sawan, et al.
Published: (2025)

Probabilistic Digital Twin for Misspecified Structural Dynamical Systems via Latent Force Modeling and Bayesian Neural Networks
by: Kashyap, Sahil, et al.
Published: (2025)

Scalable h-adaptive probabilistic solver for time-independent and time-dependent systems
by: Thakur, Akshay, et al.
Published: (2025)

Scalable Random Wavelet Features: Efficient Non-Stationary Kernel Approximation with Convergence Guarantees
by: Kumar, Sawan, et al.
Published: (2026)

Machine learning assisted state prediction of misspecified linear dynamical system via modal reduction
by: Thorat, Rohan Vitthal, et al.
Published: (2026)

Alpha-VI DeepONet: A prior-robust variational Bayesian approach for enhancing DeepONets with uncertainty quantification
by: Lone, Soban Nasir, et al.
Published: (2024)

Safe Reinforcement Learning-Based Vibration Control: Overcoming Training Risks with LQR Guidance
by: Thorat, Rohan Vitthal, et al.
Published: (2025)

Shearlet Neural Operators for Anisotropic-Shock-Dominated and Multi-scale parametric partial differential equations
by: Santos, Fabio Pereira dos, et al.
Published: (2026)

Dynamic Gaussian Graph Operator: Learning parametric partial differential equations in arbitrary discrete mechanics problems
by: Wang, Chu, et al.
Published: (2024)

MD-NOMAD: Mixture density nonlinear manifold decoder for emulating stochastic differential equations and uncertainty propagation
by: Thakur, Akshay, et al.
Published: (2024)

A recursive Bayesian neural network for constitutive modeling of sands under monotonic and cyclic loading
by: Noor, Toiba, et al.
Published: (2025)

Single-shot prediction of parametric partial differential equations
by: Rafiq, Khalid, et al.
Published: (2025)

Diffeomorphism Neural Operator for various domains and parameters of partial differential equations
by: Zhao, Zhiwei, et al.
Published: (2024)

CoNBONet: Conformalized Neuroscience-inspired Bayesian Operator Network for Reliability Analysis
by: Garg, Shailesh, et al.
Published: (2026)

Discovering governing equation in structural dynamics from acceleration-only measurements
by: Alvares, Calvin, et al.
Published: (2024)

Multifidelity Gaussian process regression for solving nonlinear partial differential equations
by: El-Boukkouri, Fatima-Zahrae, et al.
Published: (2026)

Beyond Uniform Sampling: Synergistic Active Learning and Input Denoising for Robust Neural Operators
by: Roy, Samrendra, et al.
Published: (2026)

Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations
by: Jentzen, Arnulf, et al.
Published: (2023)

Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations
by: Zhang, Huan, et al.
Published: (2024)

Adaptation of uncertainty-penalized Bayesian information criterion for parametric partial differential equation discovery
by: Thanasutives, Pongpisit, et al.
Published: (2024)

One-shot learning for solution operators of partial differential equations
by: Jiao, Anran, et al.
Published: (2021)

Differentiable Turbulence: Closure as a partial differential equation constrained optimization
by: Shankar, Varun, et al.
Published: (2023)

SPINONet: Scalable Spiking Physics-informed Neural Operator for Computational Mechanics Applications
by: Garg, Shailesh, et al.
Published: (2026)

Gaussian processes for Bayesian inverse problems associated with linear partial differential equations
by: Bai, Tianming, et al.
Published: (2023)

Latent Neural PDE Solver: a reduced-order modelling framework for partial differential equations
by: Li, Zijie, et al.
Published: (2024)

Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics
by: Liu, Xin-Yang, et al.
Published: (2022)

Generative flow induced neural architecture search: Towards discovering optimal architecture in wavelet neural operator
by: Soin, Hartej, et al.
Published: (2024)

A novel auxiliary equation neural networks method for exactly explicit solutions of nonlinear partial differential equations
by: Yuan, Shanhao, et al.
Published: (2025)

SCNO: Spiking Compositional Neural Operator -- Towards a Neuromorphic Foundation Model for Nuclear PDE Solving
by: Roy, Samrendra, et al.
Published: (2026)

CortiNet: A Physics-Perception Hybrid Cortical-Inspired Dual-Stream Network for Gallbladder Disease Diagnosis from Ultrasound
by: Kumar, Vagish, et al.
Published: (2026)

Physics Informed Neural Networks for Free Shear Flows
by: Raghu, Siddharth, et al.
Published: (2024)

Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators
by: Kobayashi, Kazuma, et al.
Published: (2025)

Distributed physics informed neural network for data-efficient solution to partial differential equations
by: Dwivedi, Vikas, et al.
Published: (2019)

Integration of physics-informed operator learning and finite element method for parametric learning of partial differential equations
by: Rezaei, Shahed, et al.
Published: (2024)

Deep Neural networks for solving high-dimensional parabolic partial differential equations
by: Zhang, Wenzhong, et al.
Published: (2026)

Operator learning for hyperbolic partial differential equations
by: Wang, Christopher, et al.
Published: (2023)

Adversarial Vulnerabilities in Neural Operator Digital Twins: Gradient-Free Attacks on Nuclear Thermal-Hydraulic Surrogates
by: Roy, Samrendra, et al.
Published: (2026)

Distribution free uncertainty quantification in neuroscience-inspired deep operators
by: Garg, Shailesh, et al.
Published: (2024)

Spatio-spectral graph neural operator for solving computational mechanics problems on irregular domain and unstructured grid
by: Sarkar, Subhankar, et al.
Published: (2024)