BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields | Paper Citation Record

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This paper cites Gaussian processes with linear operator inequality constraints.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Gaussian processes with linear operator inequality constraints

Reference 1

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This paper cites Kernels for vector-valued functions: A review.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Kernels for vector-valued functions: A review

Reference 2

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This paper cites A Bayesian approach to Lagrangian data assimilation.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields A Bayesian approach to Lagrangian data assimilation

Reference 3

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This paper cites Gaussian processes at the Helm (holtz) a more fluid model for ocean currents.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Gaussian processes at the Helm (holtz) a more fluid model for ocean currents

Reference 4

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This paper cites The Helmholtz-Hodge decomposition—a survey.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields The Helmholtz-Hodge decomposition—a survey

Reference 5

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This paper cites A causation-based computationally efficient strategy for deploying Lagrangian drifters to improve real-time state estimation.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields A causation-based computationally efficient strategy for deploying Lagrangian drifters to improve real-time state estimation

Reference 6

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This paper cites Lagrangian descriptors with uncertainty.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Lagrangian descriptors with uncertainty

Reference 7

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This paper cites Launching drifter observations in the presence of uncertainty.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Launching drifter observations in the presence of uncertainty

Reference 8

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BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Unresolved cited work

Reference 9

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This paper cites Ocean circulation kinetic energy: Reservoirs, sources, and sinks.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Ocean circulation kinetic energy: Reservoirs, sources, and sinks

Reference 10

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This paper cites Deep adaptive design: Amortizing sequential Bayesian experimental design.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Deep adaptive design: Amortizing sequential Bayesian experimental design

Reference 11

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This paper cites An unstructured-grid, finite-volume, nonhydrostatic, parallel coastal ocean simulator.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields An unstructured-grid, finite-volume, nonhydrostatic, parallel coastal ocean simulator

Reference 12

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This paper cites Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences

Reference 13

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This paper cites Lagrangian Analysis and Prediction of Coastal and Ocean Dynamics.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Lagrangian Analysis and Prediction of Coastal and Ocean Dynamics

Reference 14

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This paper cites Spatio-temporal variational Gaussian processes.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Spatio-temporal variational Gaussian processes

Reference 15

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This paper cites Physics-Informed Variational State-Space Gaussian Processes.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Physics-Informed Variational State-Space Gaussian Processes

Reference 16

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This paper cites Kalman filtering and smoothing solutions to temporal Gaussian process regression models.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Kalman filtering and smoothing solutions to temporal Gaussian process regression models

Reference 17

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This paper cites Nesting particle filters for experimental design in dynamical systems.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Nesting particle filters for experimental design in dynamical systems

Reference 18

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This paper cites Oil spill modeling: A critical review on current trends, perspectives, and challenges.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Oil spill modeling: A critical review on current trends, perspectives, and challenges

Reference 19

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This paper cites Robust and Conjugate Spatio-Temporal Gaussian Processes.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Robust and Conjugate Spatio-Temporal Gaussian Processes

Reference 20

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This paper cites A gridded surface current product for the Gulf of Mexico from consolidated drifter measurements.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields A gridded surface current product for the Gulf of Mexico from consolidated drifter measurements

Reference 21

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This paper cites Fractional Brownian motion, the Mat \'e rn process, and stochastic modeling of turbulent dispersion.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Fractional Brownian motion, the Mat \'e rn process, and stochastic modeling of turbulent dispersion

Reference 22

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This paper cites An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach

Reference 23

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BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields The SPDE approach for Gaussian and non-Gaussian fields: 10 years and still running

Reference 24

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This paper cites On a measure of the information provided by an experiment.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields On a measure of the information provided by an experiment

Reference 25

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This paper cites Advances in the application of surface drifters.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Advances in the application of surface drifters

Reference 26

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This paper cites Lagrangian descriptors: A method for revealing phase space structures of general time dependent dynamical systems.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Lagrangian descriptors: A method for revealing phase space structures of general time dependent dynamical systems

Reference 27

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This paper cites GPJax: A Gaussian Process Framework in JAX.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields GPJax: A Gaussian Process Framework in JAX

Reference 28

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This paper cites Drifter launch strategies based on Lagrangian templates.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Drifter launch strategies based on Lagrangian templates

Reference 29

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This paper cites Inferring flow energy, space scales, and timescales: freely drifting vs.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Inferring flow energy, space scales, and timescales: freely drifting vs

Reference 30

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BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Modern Bayesian experimental design

Reference 31

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BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields A seasonal harmonic model for internal tide amplitude prediction

Reference 32

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This paper cites Monte Carlo Statistical Methods , volume 2.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Monte Carlo Statistical Methods , volume 2

Reference 33

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BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Gaussian Markov Random Fields: Theory and Applications

Reference 34

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This paper cites A review of modern computational algorithms for Bayesian optimal design.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields A review of modern computational algorithms for Bayesian optimal design

Reference 35

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This paper cites Scalable inference for structured Gaussian process models.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Scalable inference for structured Gaussian process models

Reference 36

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This paper cites Using flow geometry for drifter deployment in Lagrangian data assimilation.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Using flow geometry for drifter deployment in Lagrangian data assimilation

Reference 37

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Observation ebbe38cb-61e1-4044-acb8-e32ec72f38b6 · outbound

This paper cites Applied Stochastic Differential Equations , volume 10.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Applied Stochastic Differential Equations , volume 10

Reference 38

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This paper cites Spatiotemporal learning via infinite-dimensional Bayesian filtering and smoothing: A look at Gaussian process regression through Kalman filtering.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Spatiotemporal learning via infinite-dimensional Bayesian filtering and smoothing: A look at Gaussian process regression through Kalman filtering

Reference 39

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This paper cites Active learning literature survey.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Active learning literature survey

Reference 40

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This paper cites Prediction-Centric Uncertainty Quantification via MMD.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Prediction-Centric Uncertainty Quantification via MMD

Reference 41

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This paper cites Stochastic differential equation methods for spatio-temporal Gaussian process regression.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Stochastic differential equation methods for spatio-temporal Gaussian process regression

Reference 42

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This paper cites Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

Reference 43

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This paper cites An Introduction to Numerical Analysis.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields An Introduction to Numerical Analysis

Reference 44

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This paper cites Numerical Linear Algebra , volume 50.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Numerical Linear Algebra , volume 50

Reference 45

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This paper cites An efficient drifters deployment strategy to evaluate water current velocity fields.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields An efficient drifters deployment strategy to evaluate water current velocity fields

Reference 46

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This paper cites Dispersion of surface drifters in the tropical Atlantic.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Dispersion of surface drifters in the tropical Atlantic

Reference 47

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Observation f0eaf690-95bf-4de8-b0ab-952f661c6387 · outbound

This paper cites Nearshore internal bores and turbulent mixing in southern Monterey Bay.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Nearshore internal bores and turbulent mixing in southern Monterey Bay

Reference 48

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation adb53b6f-c8d1-4cd4-8d5c-78626a216304 · outbound

This paper cites On stationary processes in the plane.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields On stationary processes in the plane

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T12:44:52.864790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 2098f280-6c3f-4287-91e3-7d71969c8229 · outbound

This paper cites Stochastic processes in several dimensions.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Stochastic processes in several dimensions

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T12:44:52.860468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation d2602f0e-a9c4-4b8a-8cb9-90841271f7e4 · outbound

This paper cites Gaussian Processes for Machine Learning , volume 2.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields Gaussian Processes for Machine Learning , volume 2

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T12:44:52.842796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation ca9d89ea-2f4a-4363-aafe-8ec286729931 · outbound

This paper cites HHD-GP: Incorporating Helmholtz-Hodge Decomposition into Gaussian Processes for Learning Dynamical Systems.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields HHD-GP: Incorporating Helmholtz-Hodge Decomposition into Gaussian Processes for Learning Dynamical Systems

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T12:44:52.856461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

Observation 1f178c85-2fec-407d-8fcb-edb39499c7c1 · outbound

This paper cites BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories.

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T12:44:52.852859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-30T06:33:22.917629+00:00.

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