arXiv research

Decision tool helps manage biofouling risks for ships in the Baltic Sea.

Study introduces a probabilistic framework for air-sea fluxes using neural networks.

Generative AI predicts Arctic sea ice dynamics over decades.

Neural-network emulators predict sea-level changes due to Antarctic ice melt.

New method corrects seasonal Arctic sea ice predictions with probabilistic models.

New model uses heteroscedastic Gaussian process for alkenone SST proxy.

Develops probabilistic forecasting for Sea Level Anomalies using Conformal Prediction on functional time series.

Study optimizes climate adaptation strategies for NYC.

Forecast dam inflow using sea surface feature weights.

Study compares machine learning algorithms for predicting SST in the Great Barrier Reef.

Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decom…

Improved regret bounds for online convex optimization under stochastic and adversarial settings.

New model combines physics and machine learning for ocean dynamics.

BALLAST optimizes Lagrangian observer placement for ocean vector fields.

Super-resolution is a classical problem in image processing, with numerous applications to remote sensing image enhancement. Here, we address the super-resolution of irregularly-sampled remote sensing images. Using an optimal interpolation as the low-resolution reconstruction, we explore locally-adapted multimodal conv…

We investigate three-dimensional surfaces where the normal vector forms a constant angle with the radius vector. These surfaces naturally extend equiangular (logarithmic) spirals in the plane.

The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges. While model-driven representations remain the classic approaches, data-driven representations become more and more appealing to benefit from available large-scale observation and simulation dat…

New neural network enforces mass conservation for better ice flow predictions.

Pattern ensembling fills in missing or inaccurate trajectory data.

Understanding local currents in the North Atlantic region of the ocean is a key part of modelling heat transfer and global climate patterns. Satellites provide a surface signature of the temperature of the ocean with a high horizontal resolution while in situ autonomous probes supply high vertical resolution, but horiz…

Study uses ANFIS to assess wind power under climate change.

New algorithms compute Koopman operators on RKHSs efficiently and accurately.

Orthogonal Matching Pursuit (OMP) plays an important role in data science and its applications such as sparse subspace clustering and image processing. However, the existing OMP-based approaches lack of data adaptiveness so that the data cannot be represented well enough and may lose the accuracy. This paper proposes a…

Neural point estimators improve parameter estimation from replicated data.

New method for PKM inverse dynamics second derivatives efficiently.

In this study, the wind data series from five locations in Aegean Sea islands, the most active `hotspots' in terms of refugee influx during the Oct/2015 - Jan/2016 period, are investigated. The analysis of the three-per-site data series includes standard statistical analysis and parametric distributions, auto-correlati…

DYffusion improves diffusion models for spatiotemporal forecasting.

Paper introduces MTCM to measure multivariate tail dependence.

There are several ways of a construction of a boundary of a symmetric space using pencils of geodesics: the Karpelevich boundary, the visibility boundary, the associahedral boundary, and the sea urchin. We give explicit descriptions of these boundaries. We obtain some moduli space like polyhedra as sections of these co…

Flexible XVAE model for efficient spatial extremes simulation.

Study analyzes GDP growth of CEE countries using time-varying coefficients.

This review assesses statistical and machine learning methods for coral bleaching.

CVAE detects weak complex signals in maritime radar, improving detection over classical methods.

A hybrid ML method improves ship response predictions across different sea conditions.

Integro-difference equation (IDE) models describe the conditional dependence between the spatial process at a future time point and the process at the present time point through an integral operator. Nonlinearity or temporal dependence in the dynamics is often captured by allowing the operator parameters to vary tempor…

We propose a formulation of a Lorentzian quantum geometry based on the framework of causal fermion systems. After giving the general definition of causal fermion systems, we deduce space-time as a topological space with an underlying causal structure. Restricting attention to systems of spin dimension two, we derive th…

Space partitions of $\mathbb{R}^d$ underlie a vast and important class of fast nearest neighbor search (NNS) algorithms. Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for building space partitions re…

Information-theoretic quantities, such as entropy, are used to quantify the amount of information a given variable provides. Entropies can be used together to compute the mutual information, which quantifies the amount of information two variables share. However, accurately estimating these quantities from data is extr…

Study examines spillovers between BRICS and U.S. staple grain futures markets.

The adaptation of numerical wind wave models to the local time-spatial conditions is a problem that can be solved by using various calibration techniques. However, the obtained sets of physical parameters become over-tuned to specific events if there is a lack of observations. In this paper, we propose a robust evoluti…

This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks. We develop a high-performance MODRL framework that supports both single-policy and multi-policy strategies, as well as both linear and non-linear approaches to action selection. The experimental …

New algorithms reduce online learning regret by tracking gradient variation.

Underwater gas reservoirs are used in many situations. In particular, Carbon Capture and Storage (CCS) facilities that are currently being developed intend to store greenhouse gases inside geological formations in the deep sea. In these formations, however, the gas might percolate, leaking back to the water and eventua…

New method forecasts multilinear data using tensor autoregression.

This study analyzes dynamic connectedness in global supply chain infrastructure portfolios, identifying key risk factors and extreme events.

HyperAgent improves RL exploration in large-scale problems.

Designing a covariance function that represents the underlying correlation is a crucial step in modeling complex natural systems, such as climate models. Geospatial datasets at a global scale usually suffer from non-stationarity and non-uniformly smooth spatial boundaries. A Gaussian process regression using a non-stat…

Sparse-mode DMD disambiguates local and global modes in spatiotemporal data.