A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
OceanForecastBench offers a comprehensive benchmark for data-driven ocean forecasting models.
OCEAN infers online task identities from context variables.
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…
Predicting illegal fishing on Patagonian Shelf using machine learning.
Novel spatio-temporal LSTM model forecasts oceanic variables across sensors and scales.
Optimizes deep learning models for ocean dynamics using Fourier neural operators.
Enhances ocean floor mapping with adaptive uncertainty estimates.
Model accurately gates ocean microbes from high-frequency flow cytometry data.
Gaussian processes improved for ocean current reconstruction and divergence identification.
Deep learning model predicts wind-wave relationship.
This article explores the concepts of ocean wave multivariate multistep forecasting, reconstruction and feature selection. We introduce recurrent neural network frameworks, integrated with Bayesian hyperparameter optimization and Elastic Net methods. We consider both short- and long-term forecasts and reconstruction, f…
BALLAST optimizes Lagrangian observer placement for ocean vector fields.
Machine learning improves sub-seasonal climate forecasting, especially gradient boosting and deep learning.
SIMPGEN improves SWOT SSH data interpretation by removing noise and preserving fine-scale features.
Study uses echo-sounder buoys to analyze tuna schools' association with dFADs globally.
The paper develops sampling methods for ocean phenomena based on temperature and salinity measurements.
Model predicts phytoplankton subpopulations based on environmental factors.
Study introduces a probabilistic framework for air-sea fluxes using neural networks.
Decadal climate predictions, which are initialized with observed conditions, are characterized by two main sources of uncertainties--internal and model variabilities. Using an ensemble of climate model simulations from the CMIP5 decadal experiments, we quantified the total uncertainty associated with these predictions …
A big challenge in environmental monitoring is the spatiotemporal variation of the phenomena to be observed. To enable persistent sensing and estimation in such a setting, it is beneficial to have a time-varying underlying environmental model. Here we present a planning and learning method that enables an autonomous ma…
ConfEviSurrogate improves surrogate model accuracy and uncertainty quantification.
Physics-constrained deep learning predicts geophysical dynamics with boundedness.
Generative model for high-dimensional categorical data using Gaussian-Dirichlet fields.
In this pedagogical study, carried out by adopting standard mathematical methods of nonlinear dynamics, we have presented some simple analytical models to understand terminal behaviour in industrial growth. This issue has also been addressed from a dynamical systems perspective, with especial emphasis on the concept of…
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…
We introduce a new strategy designed to help physicists discover hidden laws governing dynamical systems. We propose to use machine learning automatic differentiation libraries to develop hybrid numerical models that combine components based on prior physical knowledge with components based on neural networks. In these…
Machine learning enhances ocean studies by analyzing sparse, multi-scale data.
This work builds an automated anomaly detection method for chaotic time series, and more concretely for turbulent, high-dimensional, ocean simulations. We solve this task by extending the Echo State Network by spatially aware input maps, such as convolutions, gradients, cosine transforms, et cetera, as well as a spatia…
The study uses machine learning to predict CAT bond coupons based on climate data.
We describe our first-place solution to the Animal Behavior Challenge (ABC 2018) on predicting gender of bird from its GPS trajectory. The task consisted in predicting the gender of shearwater based on how they navigate themselves across a big ocean. The trajectories are collected from GPS loggers attached on shearwate…
New GPs model edge functions on complex networks, capturing divergence and curl.
Generative AI predicts Arctic sea ice dynamics over decades.
This study introduces a framework for the forecasting, reconstruction and feature engineering of multivariate processes along with its renewable energy applications. We integrate derivative-free optimization with an ensemble of sequence-to-sequence networks and design a new resampling technique called additive resampli…
We show that the climate phenomena of El Nino and La Nina arise naturally as states of macro-variables when our recent causal feature learning framework (Chalupka 2015, Chalupka 2016) is applied to micro-level measures of zonal wind (ZW) and sea surface temperatures (SST) taken over the equatorial band of the Pacific O…
This work develops discrete Gaussian models for vector-valued data on triangular meshes.
Machine learning predicts seasonal precipitation for East Africa.
We generalize Conway's approach to integral binary quadratic forms on Q to study integral binary hermitian forms on quadratic imaginary extensions of Q. In Conway's case, an indefinite form that doesn't represent 0 determines a line ("river") in the spine T associated with SL(2,Z) in the hyperbolic plane. In our genera…
Study uses DMD to analyze oceanic features in Strait of Gibraltar.
New method predicts wave height exceedance probabilities.
Water managers in the western United States (U.S.) rely on longterm forecasts of temperature and precipitation to prepare for droughts and other wet weather extremes. To improve the accuracy of these longterm forecasts, the U.S. Bureau of Reclamation and the National Oceanic and Atmospheric Administration (NOAA) launch…
New method models intensity functions on spheres using normalizing flows.
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…
M-CaStLe discovers causal structures in multivariate space-time data.
We derive a single pass algorithm for computing the gradient and Fisher information of Vecchia's Gaussian process loglikelihood approximation, which provides a computationally efficient means for applying the Fisher scoring algorithm for maximizing the loglikelihood. The advantages of the optimization techniques are de…
Lagrangian data assimilation is a complex problem in oceanic and atmospheric modeling. Tracking drifters in large-scale geophysical flows can involve uncertainty in drifter location, complex inertial effects, and other factors which make comparing them to simulated Lagrangian trajectories from numerical models extremel…
When a feed-forward neural network (FNN) is trained for source ranging in an ocean waveguide, it is difficult evaluating the range accuracy of the FNN on unlabeled test data. A fitting-based early stopping (FEAST) method is introduced to evaluate the range error of the FNN on test data where the distance of source is u…
We give a graphical theory of integral indefinite binary Hamiltonian forms analogous to the one by Conway for binary quadratic forms and the one of Bestvina-Savin for binary Hermitian forms. Given a maximal order in a definite quaternion algebra over , we define the waterworld of , analog…
We first introduce a novel profile-based alignment algorithm, the multiple continuous Signal Alignment algorithm with Gaussian Process Regression profiles (SA-GPR). SA-GPR addresses the limitations of currently available signal alignment methods by adopting a hybrid of the particle smoothing and Markov-chain Monte Carl…