Copertina

Multi-fidelity Batch Active Learning for Gaussian Process Classifiers

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Cutforth, Murray, Yang, Yiming, Fan, Tiffany, Guillas, Serge, Darve, Eric
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Computational Engineering, Finance, and Science Computational Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Physically Interpretable Representation and Controlled Generation for Turbulence Data
di: Fan, Tiffany, et al.
Pubblicazione: (2025)

Multi-fidelity Hamiltonian Monte Carlo
di: Patel, Dhruv V., et al.
Pubblicazione: (2024)

Convolutional autoencoders for the reconstruction of three-dimensional interfacial multiphase flows
di: Cutforth, Murray, et al.
Pubblicazione: (2025)

Physics-constrained Gaussian Processes for Predicting Shockwave Hugoniot Curves
di: Pasparakis, George D., et al.
Pubblicazione: (2026)

Graph Laplacian-based Bayesian Multi-fidelity Modeling
di: Pinti, Orazio, et al.
Pubblicazione: (2024)

Solving High Frequency and Multi-Scale PDEs with Gaussian Processes
di: Fang, Shikai, et al.
Pubblicazione: (2023)

Free-form Grid Structure Form Finding based on Machine Learning and Multi-objective Optimisation
di: Meng, Yiping, et al.
Pubblicazione: (2024)

Efficient Aircraft Design Optimization Using Multi-Fidelity Models and Multi-fidelity Physics Informed Neural Networks
di: Sarker, Apurba
Pubblicazione: (2024)

Multi-fidelity approaches for general constrained Bayesian optimization with application to aircraft design
di: Cordelier, Oihan, et al.
Pubblicazione: (2026)

Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process
di: Comlek, Yigitcan, et al.
Pubblicazione: (2024)

Multi-fidelity Bayesian Optimization: A Review
di: Do, Bach, et al.
Pubblicazione: (2023)

Physics-Informed Deep Contrast Source Inversion: A Unified Framework for Inverse Scattering Problems
di: Sun, Haoran, et al.
Pubblicazione: (2025)

Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU)
di: Tan, Jingye, et al.
Pubblicazione: (2026)

Gaussian Process Surrogate Models for Efficient Estimation of Structural Response Distributions and Order Statistics
di: Flovik, Vegard, et al.
Pubblicazione: (2025)

A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications
di: Logakannan, Krishna Prasath, et al.
Pubblicazione: (2025)

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations
di: Lei, Bo, et al.
Pubblicazione: (2025)

A Domain Adaptation of Large Language Models for Classifying Mechanical Assembly Components
di: Elhambakhsh, Fatemeh, et al.
Pubblicazione: (2025)

Equi-Euler GraphNet: An Equivariant, Temporal-Dynamics Informed Graph Neural Network for Dual Force and Trajectory Prediction in Multi-Body Systems
di: Sharma, Vinay, et al.
Pubblicazione: (2025)

Optimizing Portfolio Management and Risk Assessment in Digital Assets Using Deep Learning for Predictive Analysis
di: Cheng, Qishuo, et al.
Pubblicazione: (2024)

Adapting Automotive Aerodynamics Surrogates to New Vehicle Families via Transfer Learning
di: Keum, Seunghwan, et al.
Pubblicazione: (2026)

Applied Machine Learning to Anomaly Detection in Enterprise Purchase Processes
di: Herreros-Martínez, A., et al.
Pubblicazione: (2024)

Accelerated Patient-Specific Hemodynamic Simulations with Hybrid Physics-Based Neural Surrogates
di: Rubio, Natalia L., et al.
Pubblicazione: (2026)

Accurate and scalable deep Maxwell solvers using multilevel iterative methods
di: Mao, Chenkai, et al.
Pubblicazione: (2025)

Physics-informed Partitioned Coupled Neural Operator for Complex Networks
di: Wu, Weidong, et al.
Pubblicazione: (2024)

Hybrid Fourier Neural Operator for Surrogate Modeling of Laser Processing with a Quantum-Circuit Mixer
di: Papierz, Mateusz, et al.
Pubblicazione: (2026)

Application of Tensor Neural Networks to Pricing Bermudan Swaptions
di: Patel, Raj G., et al.
Pubblicazione: (2023)

Graph Multi-Similarity Learning for Molecular Property Prediction
di: Xu, Hao, et al.
Pubblicazione: (2024)

Diffusion-Augmented Reinforcement Learning for Robust Portfolio Optimization under Stress Scenarios
di: Choudhary, Himanshu, et al.
Pubblicazione: (2025)

Hierarchical Stacking Optimization Using Dirichlet's Process (SoDip): Towards Accelerated Design for Graft Polymerization
di: Ibrahim, Amgad Ahmed Ali, et al.
Pubblicazione: (2025)

Deep Reinforcement Learning Based Toolpath Generation for Thermal Uniformity in Laser Powder Bed Fusion Process
di: Qin, Mian, et al.
Pubblicazione: (2024)

Physics-Informed Machine Learning Towards A Real-Time Spacecraft Thermal Simulator
di: Oddiraju, Manaswin, et al.
Pubblicazione: (2024)

LASER: Learning Active Sensing for Continuum Field Reconstruction
di: Deng, Huayu, et al.
Pubblicazione: (2026)

DADO -- Low-Cost Query Strategies for Deep Active Design Optimization
di: Decke, Jens, et al.
Pubblicazione: (2023)

Aerodynamic force reconstruction using physics-informed Gaussian processes
di: Tondo, Gledson Rodrigo, et al.
Pubblicazione: (2026)

Discovering uncertainty: Gaussian constitutive neural networks with correlated weights
di: McCulloch, Jeremy A., et al.
Pubblicazione: (2025)

Efficient Inverse Design Optimization through Multi-fidelity Simulations, Machine Learning, and Search Space Reduction Strategies
di: Grbcic, Luka, et al.
Pubblicazione: (2023)

Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud Detection
di: Amekoe, Kodjo Mawuena, et al.
Pubblicazione: (2024)

Scale-PINN: Learning Efficient Physics-Informed Neural Networks Through Sequential Correction
di: Chiu, Pao-Hsiung, et al.
Pubblicazione: (2026)

Multi-Objective Reinforcement Learning-based Approach for Pressurized Water Reactor Optimization
di: Seurin, Paul, et al.
Pubblicazione: (2023)

Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles
di: Park, Junghoon Justin, et al.
Pubblicazione: (2025)