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arXiv research

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.

168,695 papers · 148 categories

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9 results for “WRF”

Data Evolution by Wittgenstein's Rule Followingstat.ML

Wittgenstein's Rule Following evolves datasets by extrapolating structural descriptors.

problem Generating meaningful continuations of evolving datasets.

method Wittgenstein's Rule Following (WRF) uses structural descriptors to extrapolate trajectories and average historical descriptors.

result WRF can generate meaningful continuations of evolving datasets.

Wittgenstein Rule Following Data Evolution Structural Descriptors Mixture Recombination

2026-06-21abs ↗pdf ↗

Uncertainty Quantification of Wind Gust Predictions in the Northeast United States: An Evidential Neural Network and Explainable Artificial Intelligence Approachcs.LG

Novel approach uses ENN for UQ in gust predictions, reducing RMSE and improving confidence.

problem Reducing bias and uncertainty in wind gust predictions.

method Evidential Neural Network (ENN) with Explainable AI.

result 47% reduction in RMSE, 95% coverage of observed gusts at 179 out of 266 stations.

wind gusts uncertainty quantification machine learning ENN WRF

2025-02-01abs ↗pdf ↗

Machine learning-based probabilistic forecasting of solar irradiance in Chilestat.AP

The study improves solar irradiance forecasts for Chile using machine learning.

problem Accurate short-term PV power forecasts for Chile's Atacama Desert.

method 8-member ensemble forecasts of solar irradiance using WRF model, calibrated with EMOS and DRN.

result Machine learning-based post-processing methods improve forecast accuracy and calibration.

solar irradiance probabilistic forecasting ensemble forecasts WRF model EMOS

2024-11-17abs ↗pdf ↗

Optimal Weighted Random Forestsstat.ML

Optimal weighted random forests improve prediction accuracy.

problem Unequal prediction performance among random forest trees.

method Proposes 1-step and 2-step optimal weighting algorithms.

result Asymptotically optimal in terms of squared loss and risk.

random forest regression prediction accuracy weighting model averaging

2023-05-17abs ↗pdf ↗

An unsupervised learning approach for predicting wind farm power and downstream wakes using weather patternsstat.ML

New method predicts wind farm power and wakes using weather patterns.

problem Inefficient and computationally intensive wind energy resource assessment.

method Unsupervised clustering of ERA5 data on wind velocity, WRF simulations at cluster centers, and post-processing.

result Accurate long-term predictions of power and wakes with reduced computational time.

wind energy unsupervised learning weather patterns wind farm power prediction

2023-02-12abs ↗pdf ↗

Computer Model Calibration with Time Series Data using Deep Learning and Quantile Regressionstat.ML

New framework calibrates computer models using deep learning and quantile regression.

problem Uncertainty in computer model input parameters due to high-dimensional time series data.

method Deep neural network with long-short term memory layers for inverse modeling, quantile regression for interval predictions.

result Accurate point and interval estimates for input parameters in WRF-hydro model.

deep learning quantile regression computer model calibration time series data WRF-hydro

2020-08-29abs ↗pdf ↗

A Composite-Loss Graph Neural Network for the Multivariate Post-Processing of Ensemble Weather Forecastsstat.AP

A graph neural network improves multivariate post-processing of ensemble forecasts.

problem Systematic biases in ensemble forecasts and loss of dependencies across forecast dimensions.

method A composite-Loss Graph Neural Network (dualGNN) trained with a composite loss function combining ES and VS.

result The dualGNN outperforms traditional methods in multivariate verification metrics and captures spatial relationships.

graph neural network ensemble forecasting multivariate post-processing composite loss variogram score

2025-09-02abs ↗pdf ↗

A Deep Learning Approach to Probabilistic Forecasting of Weatherstat.ML

A deep learning method for probabilistic weather forecasting.

problem Probabilistic forecasting of weather.

method Two chained machine-learning steps: dimension reduction and density estimation using normalizing flows.

result The method produces accurate conditional forecast distributions for weather.

weather forecasting probabilistic deep learning dimension reduction density estimation

2022-03-23abs ↗pdf ↗

Modeling Spatial Extremes using Non-Gaussian Spatial Autoregressive Models via Convolutional Neural Networksstat.ML

Modeling spatial extremes with non-Gaussian fields using SAR models and CNNs.

problem Challenges in modeling spatial data with heavy-tailed distributions and missing cells.

method Spatial autoregressive models with Generalized Extreme Value innovations, combined with CNN for fast parameter estimation.

result Effective modeling of spatial extremes in non-Gaussian fields, demonstrated on precipitation data.

Spatial Extremes Non-Gaussian Fields SAR Models CNNs Heavy-tailed Innovations

2025-05-05abs ↗pdf ↗