Solving High Frequency and Multi-Scale PDEs with Gaussian Processes
Fuente:
arXiv
Saved in:
| Main Authors: | Fang, Shikai, Cooley, Madison, Long, Da, Li, Shibo, Kirby, Robert, Zhe, Shandian |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications
by: Logakannan, Krishna Prasath, et al.
Published: (2025)
by: Logakannan, Krishna Prasath, et al.
Published: (2025)
Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements
by: Li, Xuyang, et al.
Published: (2025)
by: Li, Xuyang, et al.
Published: (2025)
Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process
by: Comlek, Yigitcan, et al.
Published: (2024)
by: Comlek, Yigitcan, et al.
Published: (2024)
HyResPINNs: A Hybrid Residual Physics-Informed Neural Network Architecture Designed to Balance Expressiveness and Trainability
by: Cooley, Madison, et al.
Published: (2024)
by: Cooley, Madison, et al.
Published: (2024)
Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases
by: Cooley, Madison, et al.
Published: (2024)
by: Cooley, Madison, et al.
Published: (2024)
Multi-fidelity Batch Active Learning for Gaussian Process Classifiers
by: Cutforth, Murray, et al.
Published: (2025)
by: Cutforth, Murray, et al.
Published: (2025)
STAResNet: a Network in Spacetime Algebra to solve Maxwell's PDEs
by: Pepe, Alberto, et al.
Published: (2024)
by: Pepe, Alberto, et al.
Published: (2024)
Gaussian Process Surrogate Models for Efficient Estimation of Structural Response Distributions and Order Statistics
by: Flovik, Vegard, et al.
Published: (2025)
by: Flovik, Vegard, et al.
Published: (2025)
Solving Differential Equations with Constrained Learning
by: Moro, Viggo, et al.
Published: (2024)
by: Moro, Viggo, et al.
Published: (2024)
Finite Operator Learning: Bridging Neural Operators and Numerical Methods for Efficient Parametric Solution and Optimization of PDEs
by: Rezaei, Shahed, et al.
Published: (2024)
by: Rezaei, Shahed, et al.
Published: (2024)
Multi-Fidelity Bayesian Neural Network for Uncertainty Quantification in Transonic Aerodynamic Loads
by: Vaiuso, Andrea, et al.
Published: (2024)
by: Vaiuso, Andrea, et al.
Published: (2024)
High-Dimensional Bayesian Optimisation with Large-Scale Constraints -- An Application to Aeroelastic Tailoring
by: Maathuis, Hauke, et al.
Published: (2023)
by: Maathuis, Hauke, et al.
Published: (2023)
Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation
by: Wang, Zihao, et al.
Published: (2024)
by: Wang, Zihao, et al.
Published: (2024)
A clustering adaptive Gaussian process regression method: response patterns based real-time prediction for nonlinear solid mechanics problems
by: Li, Ming-Jian, et al.
Published: (2024)
by: Li, Ming-Jian, et al.
Published: (2024)
Multi-task Meta Label Correction for Time Series Prediction
by: Yang, Luxuan, et al.
Published: (2023)
by: Yang, Luxuan, et al.
Published: (2023)
Physics-constrained Gaussian Processes for Predicting Shockwave Hugoniot Curves
by: Pasparakis, George D., et al.
Published: (2026)
by: Pasparakis, George D., et al.
Published: (2026)
MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model
by: Li, Junjie, et al.
Published: (2024)
by: Li, Junjie, et al.
Published: (2024)
Physics-informed Deep Learning to Solve Three-dimensional Terzaghi Consolidation Equation: Forward and Inverse Problems
by: Yuan, Biao, et al.
Published: (2024)
by: Yuan, Biao, et al.
Published: (2024)
HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs
by: Zhao, Jinpai, et al.
Published: (2026)
by: Zhao, Jinpai, et al.
Published: (2026)
Time Series Analysis in Frequency Domain: A Survey of Open Challenges, Opportunities and Benchmarks
by: Zhang, Qianru, et al.
Published: (2025)
by: Zhang, Qianru, et al.
Published: (2025)
Parametric Nonlinear Volterra Series via Machine Learning: Transonic Aerodynamics
by: Immordino, Gabriele, et al.
Published: (2024)
by: Immordino, Gabriele, et al.
Published: (2024)
Input Convex Lipschitz Recurrent Neural Networks for Robust and Efficient Process Modeling and Optimization
by: Wang, Zihao, et al.
Published: (2024)
by: Wang, Zihao, et al.
Published: (2024)
Aerodynamic force reconstruction using physics-informed Gaussian processes
by: Tondo, Gledson Rodrigo, et al.
Published: (2026)
by: Tondo, Gledson Rodrigo, et al.
Published: (2026)
Discovering uncertainty: Gaussian constitutive neural networks with correlated weights
by: McCulloch, Jeremy A., et al.
Published: (2025)
by: McCulloch, Jeremy A., et al.
Published: (2025)
PDE-constrained Gaussian process surrogate modeling with uncertain data locations
by: Ye, Dongwei, et al.
Published: (2023)
by: Ye, Dongwei, et al.
Published: (2023)
FinTeam: A Multi-Agent Collaborative Intelligence System for Comprehensive Financial Scenarios
by: Wu, Yingqian, et al.
Published: (2025)
by: Wu, Yingqian, et al.
Published: (2025)
A Multi-Layer Machine Learning and Econometric Pipeline for Forecasting Market Risk: Evidence from Cryptoasset Liquidity Spillovers
by: Qiu, Yimeng, et al.
Published: (2025)
by: Qiu, Yimeng, et al.
Published: (2025)
Generative Spatio-temporal GraphNet for Transonic Wing Pressure Distribution Forecasting
by: Immordino, Gabriele, et al.
Published: (2024)
by: Immordino, Gabriele, et al.
Published: (2024)
Predicting Transonic Flowfields in Non-Homogeneous Unstructured Grids Using Autoencoder Graph Convolutional Networks
by: Immordino, Gabriele, et al.
Published: (2024)
by: Immordino, Gabriele, et al.
Published: (2024)
Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
by: Cooley, Madison, et al.
Published: (2024)
by: Cooley, Madison, et al.
Published: (2024)
Applied Machine Learning to Anomaly Detection in Enterprise Purchase Processes
by: Herreros-Martínez, A., et al.
Published: (2024)
by: Herreros-Martínez, A., et al.
Published: (2024)
GPLaSDI: Gaussian Process-based Interpretable Latent Space Dynamics Identification through Deep Autoencoder
by: Bonneville, Christophe, et al.
Published: (2023)
by: Bonneville, Christophe, et al.
Published: (2023)
Deep encoder-decoder hierarchical convolutional neural networks for conjugate heat transfer surrogate modeling
by: Ebbs-Picken, Takiah, et al.
Published: (2023)
by: Ebbs-Picken, Takiah, et al.
Published: (2023)
Neural Processes Maintain Calibrated Biomass Estimates Across Spatiotemporal Gaps and Disturbance
by: Young, Robin, et al.
Published: (2026)
by: Young, Robin, et al.
Published: (2026)
Cohort-attention Evaluation Metric against Tied Data: Studying Performance of Classification Models in Cancer Detection
by: Wei, Longfei, et al.
Published: (2025)
by: Wei, Longfei, et al.
Published: (2025)
Generative Large-Scale Pre-trained Models for Automated Ad Bidding Optimization
by: Lei, Yu, et al.
Published: (2025)
by: Lei, Yu, et al.
Published: (2025)
PIVONet: A Physically-Informed Variational Neuro ODE Model for Efficient Advection-Diffusion Fluid Simulation
by: Cheung, Hei Shing, et al.
Published: (2026)
by: Cheung, Hei Shing, et al.
Published: (2026)
Linking Across Data Granularity: Fitting Multivariate Hawkes Processes to Partially Interval-Censored Data
by: Calderon, Pio, et al.
Published: (2021)
by: Calderon, Pio, et al.
Published: (2021)
Deep Reinforcement Learning Based Toolpath Generation for Thermal Uniformity in Laser Powder Bed Fusion Process
by: Qin, Mian, et al.
Published: (2024)
by: Qin, Mian, et al.
Published: (2024)
Multi-fidelity Hamiltonian Monte Carlo
by: Patel, Dhruv V., et al.
Published: (2024)
by: Patel, Dhruv V., et al.
Published: (2024)
Similar Items
-
A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications
by: Logakannan, Krishna Prasath, et al.
Published: (2025) -
Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements
by: Li, Xuyang, et al.
Published: (2025) -
Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process
by: Comlek, Yigitcan, et al.
Published: (2024) -
HyResPINNs: A Hybrid Residual Physics-Informed Neural Network Architecture Designed to Balance Expressiveness and Trainability
by: Cooley, Madison, et al.
Published: (2024) -
Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases
by: Cooley, Madison, et al.
Published: (2024)