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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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4 results for “Hydrogeological”

Uncertainty Quantification of Darcy Flow through Porous Media using Deep Gaussian Processstat.ML

Deep Gaussian processes reduce uncertainty in porous media flow modeling.

problem Uncertainty quantification in flow through heterogeneous porous media.

method Multi-layer hierarchical Gaussian process with variational approximation.

result Automatic selection of hidden layer dimensions and uncertainty propagation.

Uncertainty quantification Deep Gaussian processes Gaussian process Porous media Hydrogeological

2020-11-03abs ↗pdf ↗

A Deep Generative Model for the Simulation of Discrete Karst Networksstat.ML

Deep learning models simulate complex karst network patterns.

problem Complex karst network patterns due to hydrogeological conditions.

method Graph generative models (GraphRNN and G-DDPM) to capture topological and spatial properties.

result Stochastic simulation of karst networks across various formations.

karst networks deep learning graph generative models hydrogeology discrete networks

2025-06-11abs ↗pdf ↗

Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimizationstat.ML

We focus on kernel methods for set-valued inputs and their application to Bayesian set optimization, notably combinatorial optimization. We investigate two classes of set kernels that both rely on Reproducing Kernel Hilbert Space embeddings, namely the ``Double Sum'' (DS) kernels recently considered in Bayesian set opt…

2019-10-09abs ↗pdf ↗

Tackling water table depth modeling via machine learning: From proxy observations to verifiabilitycs.LG

ML models predict water table depth more accurately than PB models, especially in data-scarce regions.

problem Uncertainty in large-scale simulations of water table depth due to biased observational data and model flexibility.

method Constructed three ML models using XGBoost and over 20 million real and proxy observations across the US and Canada.

result ML models correlate 0.6-0.75 with real and proxy observations, significantly better than PB models (0.21-0.40).

water table depth machine learning proxy observations hydrogeology model verification

2024-04-30abs ↗pdf ↗