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.
Combines machine learning and convex limiting for accurate subgrid flux modeling in shallow-water equations.
Generative model improves noise estimation in stochastic rotating shallow water models.
New method learns soliton dynamics from scattering data without assuming known equations.
We revisit the geodesic approach to ideal hydrodynamics and present a related geometric framework for Newton's equations on groups of diffeomorphisms and spaces of probability densities. The latter setting is sufficiently general to include equations of compressible and incompressible fluid dynamics, magnetohydrodynami…
Shallow water environments create a challenging channel for communications. In this paper, we focus on the challenges posed by the frequency-selective signal distortion called the Doppler effect. We explore the design and performance of machine learning (ML) based demodulation methods --- (1) Deep Belief Network-feed f…
Hyperbolic conservation laws posed on manifolds arise in many applications to geophysical flows and general relativity. Recent work by the author and his collaborators attempts to set the foundations for a study of weak solutions defined on Riemannian or Lorentzian manifolds and includes an investigation of the existen…
Geodesic interpretation of global quasi-geostrophic equations on sphere.
A novel algorithm uses Gaussian process regression to interpret non-intrusive ROMs.
We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations. In this second part of our two-part treatise, we focus on the problem of data-driven discovery of …
New metric for probability measures connects physics and geometry.
The study forecasts water quality from satellite data using machine learning.
RandNet-Parareal uses neural networks to speed up time-parallel PDE solving.
Data-driven discovery of differential equations has been an emerging research topic. We propose a novel algorithm subsampling-based threshold sparse Bayesian regression (SubTSBR) to tackle high noise and outliers. The subsampling technique is used for improving the accuracy of the Bayesian learning algorithm. It has tw…
Latent-EnSF improves data assimilation for high-dimensional systems with sparse observations.
Many models in mathematical physics are given as non-linear partial differential equation of hydrodynamic type; the incompressible Euler, KdV, and Camassa--Holm equations are well-studied examples.A beautiful approach to well-posedness is to go from the Eulerian to a Lagrangian description.Geometrically it corresponds …
Research develops a water quality prediction model using LSTM.
Deep learning enhances water resources management through data analysis.
Artificial neural networks estimate model parameters from observations, reducing model errors.
Considering the interdependencies between water and electricity use is critical for ensuring conservation measures are successful in lowering the net water and electricity use in a city. This water-electricity demand nexus will become even more important as cities continue to grow, causing water and electricity utiliti…
According to the United Nations World Water Assessment Programme, every day, 2 million tons of sewage and industrial and agricultural waste are discharged into the worlds water. In order to address this pervasive issue of increasing water pollution, while ensuring that the global population has an efficient, accurate, …
Model forecasts water demand with probabilistic multi-step-ahead approach.
In this paper, we present a regression framework involving several machine learning models to estimate water parameters based on hyperspectral data. Measurements from a multi-sensor field campaign, conducted on the River Elbe, Germany, represent the benchmark dataset. It contains hyperspectral data and the five water p…
A model for groundwater trading among stakeholders.
Predict water pipe failures using machine learning and survival analysis.
Water balance models (WBMs) are often employed to understand regional hydrologic cycles over various time scales. Most WBMs, however, are physically-based, and few employ state-of-the-art statistical methods to reconcile independent input measurement uncertainty and bias. Further, few WBMs exist for large lakes, and mo…
Quantile regression improves urban water demand forecasting.
Energy consumption for hot water production is a major draw in high efficiency buildings. Optimizing this has typically been approached from a thermodynamics perspective, decoupled from occupant influence. Furthermore, optimization usually presupposes existence of a detailed dynamics model for the hot water system. The…
Cohesion uses deep Koopman operators to generate long-range forecasts of chaotic dynamics.
When the residents of Flint learned that lead had contaminated their water system, the local government made water-testing kits available to them free of charge. The city government published the results of these tests, creating a valuable dataset that is key to understanding the causes and extent of the lead contamina…
Solving systems of linear equations is a problem occuring frequently in water engineering applications. Usually the size of the problem is too large to be solved via direct factorization. One can resort to iterative approaches, in particular the conjugate gradients method if the matrix is symmetric positive definite. P…
Graph Neural Simulators improve data efficiency for PDE surrogates.
LightGBM outperforms other models in predicting pH values in Georgia, USA.
Purpose: To investigate the feasibility of myelin water content quantification using fast dual-echo steady-state (DESS) scans and machine learning with kernels. Methods: We optimized combinations of steady-state (SS) scans for precisely estimating the fast-relaxing signal fraction ff of a two-compartment signal model, …
In numerical modeling of the Earth System, many processes remain unknown or ill represented (let us quote sub-grid processes, the dependence to unknown latent variables or the non-inclusion of complex dynamics in numerical models) but sometimes can be observed. This paper proposes a methodology to produce a hybrid mode…
Study on dynamics of non-linear autoencoders learning principal components.
NKN deep neural network learns governing equations and classifies images.
Light pillars over rippled water appear parallel due to projection geometry.
Machine learning predicts liquid water properties from cluster data.
Water saturation is an important property in reservoir engineering domain. Thus, satisfactory classification of water saturation from seismic attributes is beneficial for reservoir characterization. However, diverse and non-linear nature of subsurface attributes makes the classification task difficult. In this context,…
As the issue of freshwater shortage is increasing daily, it is critical to take effective measures for water conservation. According to previous studies, device level consumption could lead to significant freshwater conservation. Existing water disaggregation methods focus on learning the signatures for appliances; how…
Study predicts coastal water quality using machine learning, identifying salinity as key factor.
Machine learning models estimate nutrient concentrations from water quality surrogates.
This paper concerns the development and application of the multisymplectic Lagrangian and Hamiltonian formalism for nonlinear partial differential equations. In this theory, solutions of a PDE are sections of a fiber bundle over a base manifold of dimension +$1, typically taken to be spacetime. Given a conn…
Deep learning (DL), a new-generation of artificial neural network research, has transformed industries, daily lives and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem-relevant features and capture highly complex data…
Hybrid model predicts flow and pressure in water systems.
Method reduces model bias in water temperature prediction using physics-guided GNNs.
We present an approach based on machine learning (ML) to distinguish eruption and precursory signals of Chimayó geyser (New Mexico, USA) under noisy environments. This geyser can be considered as a natural analog of intrusion into shallow water aquifers. By studying this geyser, we can understand upwell…
Low-cost water-level tracking using LTE power metrics and wavelet analysis.