{"total":41,"rows":[{"id":"2212.14319","title":"Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients","created":"2022-12-29","cited_by_count":17,"area":null},{"id":"2309.10656","title":"A spectrum of physics-informed Gaussian processes for regression in engineering","created":"2023-09-19","cited_by_count":14,"area":null},{"id":"2311.04465","title":"Solving High Frequency and Multi-Scale PDEs with Gaussian Processes","created":"2023-11-08","cited_by_count":14,"area":null},{"id":"2406.05072","title":"Linearization Turns Neural Operators into Function-Valued Gaussian Processes","created":"2024-06-07","cited_by_count":12,"area":null},{"id":"2304.10242","title":"Fourier Neural Operator Surrogate Model to Predict 3D Seismic Waves Propagation","created":"2023-04-20","cited_by_count":11,"area":null},{"id":"2312.14886","title":"Sample Path Regularity of Gaussian Processes from the Covariance Kernel","created":"2023-12-22","cited_by_count":11,"area":null},{"id":"2407.13711","title":"FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning","created":"2024-07-18","cited_by_count":11,"area":null},{"id":"2411.01036","title":"Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference","created":"2024-11-01","cited_by_count":9,"area":null},{"id":"2409.13876","title":"Physics-Informed Variational State-Space Gaussian Processes","created":"2024-09-20","cited_by_count":7,"area":null},{"id":"2312.12193","title":"Gaussian process learning of nonlinear dynamics","created":"2023-12-19","cited_by_count":5,"area":null},{"id":"2404.15618","title":"Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations","created":"2024-04-24","cited_by_count":4,"area":null},{"id":"2510.25368","title":"Position: Biology is the Challenge Physics-Informed ML Needs to Evolve","created":"2025-10-29","cited_by_count":4,"area":null},{"id":"2605.21041","title":"Conditioning Gaussian Processes on Almost Anything","created":"2026-05-20","cited_by_count":4,"area":null},{"id":"2305.12585","title":"Equivariant geometric convolutions for emulation of dynamical systems","created":"2023-05-21","cited_by_count":3,"area":null},{"id":"2409.10972","title":"Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics","created":"2024-09-17","cited_by_count":3,"area":null},{"id":"2510.26121","title":"Uncertainty-Aware Diagnostics for Physics-Informed Machine Learning","created":"2025-10-30","cited_by_count":3,"area":null},{"id":"2406.05020","title":"Scaling up Probabilistic PDE Simulators with Structured Volumetric Information","created":"2024-06-07","cited_by_count":2,"area":null},{"id":"2411.16663","title":"Gaussian Process Priors for Boundary Value Problems of Linear Partial Differential Equations","created":"2024-11-25","cited_by_count":2,"area":null},{"id":"2408.14889","title":"Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6","created":"2024-08-27","cited_by_count":1,"area":null},{"id":"2503.08343","title":"Flexible and Efficient Probabilistic PDE Solvers through Gaussian Markov Random Fields","created":"2025-03-11","cited_by_count":1,"area":null},{"id":"2503.14453","title":"Online Conformal Probabilistic Numerics via Adaptive Edge-Cloud Offloading","created":"2025-03-18","cited_by_count":1,"area":null},{"id":"2504.08766","title":"Towards scientific machine learning for granular material simulations -- challenges and opportunities","created":"2025-04-01","cited_by_count":1,"area":null},{"id":"2505.21133","title":"Robust and Computation-Aware Gaussian Processes","created":"2025-05-27","cited_by_count":1,"area":null},{"id":"2506.18247","title":"Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures","created":"2025-06-23","cited_by_count":1,"area":null},{"id":"2509.02617","title":"Gaussian process surrogate with physical law-corrected prior for multi-coupled PDEs defined on irregular geometry","created":"2025-09-01","cited_by_count":1,"area":null},{"id":"2509.14054","title":"Physics-based deep kernel learning for parameter estimation in high dimensional PDEs","created":"2025-09-17","cited_by_count":1,"area":null},{"id":"2510.13601","title":"Physics-augmented Multi-task Gaussian Process for Modeling Spatiotemporal Dynamics","created":"2025-10-15","cited_by_count":1,"area":null},{"id":"2503.04684","title":"Propagating Model Uncertainty through Filtering-based Probabilistic Numerical ODE Solvers","created":"2025-03-06","cited_by_count":0,"area":null},{"id":"2508.09623","title":"Scalable h-adaptive probabilistic solver for time-independent and time-dependent systems","created":"2025-08-13","cited_by_count":0,"area":null},{"id":"2511.04518","title":"Comparing EPGP Surrogates and Finite Elements Under Degree-of-Freedom Parity","created":"2025-11-06","cited_by_count":0,"area":null},{"id":"2512.13217","title":"Rethinking Physics-Informed Regression Beyond Training Loops and Bespoke Architectures","created":"2025-12-15","cited_by_count":0,"area":null},{"id":"2601.05297","title":"Machine learning assisted state prediction of misspecified linear dynamical system via modal reduction","created":"2026-01-08","cited_by_count":0,"area":null},{"id":"2601.06462","title":"Physics-informed Gaussian Process Regression in Solving Eigenvalue Problem of Linear Operators","created":"2026-01-10","cited_by_count":0,"area":null},{"id":"2603.22050","title":"Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data","created":"2026-03-23","cited_by_count":0,"area":null},{"id":"2605.06246","title":"Structure-Preserving Gaussian Processes Via Discrete Euler-Lagrange Equations","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.31127","title":"Scalable Bayesian Inference for Nonlinear Conservation Laws","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2607.06292","title":"A unified perspective of Gaussian process approximation for differential equations","created":"2026-07-07","cited_by_count":0,"area":null},{"id":"2607.10793","title":"Hierarchical Bayesian Quadrature","created":"2026-07-12","cited_by_count":0,"area":null},{"id":"2607.14193","title":"Operator-Informed Gaussian Processes for Complex Helmholtz Wavefields: From Synthetic Benchmarks to In Vivo Brain Elastography","created":"2026-07-15","cited_by_count":0,"area":null},{"id":"2609.16406","title":"Physics Informed Random Feature Neural Networks for Solving PDEs","created":"2026-09-14","cited_by_count":0,"area":null},{"id":"2610.13804","title":"Constrained Probabilistic Numerics: Gaussian Process Solvers for Linear PDEs under Linear Inequality Constraints","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}