Programmatuur voor het nesten van 3D modellen in TRIWAQ

Overzetten bodempeilingen Waal/Pannerdensch kanaal in GRID

Onafhankelijk onderzoek Markermeer - technisch inhoudelijke en integrerende studie, verslag fase 1c: Gereed maken HYDRA_M

Modification source code SWAN cycle 2

Model convergence of SWAN in the Westerschelde estuary: A study on model convergence using the one-dimensional mode of SWAN

A new vertical approximation for the numerical simulation of non-hydrostatic free surface flows

FEWS-Rhine version 1.02: Improvements and adjustments

Fourier reconstruction with sparse inversions

In seismic exploration an image of the subsurface is generated from seismic data through various data processing algorithms. When the data is not acquired on an equidistantly spaced grid, artifacts may result in the final image. Fourier reconstruction is an interpolation technique that can reduce these artifacts by generating uniformly sampled...

A Scattered Data Interpolant for the Solution of Three-Dimensional PDEs

Using a Differential Equation Interpolant (DEI), one can accurately approximate the solution of a Partial Differential Equation (PDE) at off-mesh points. The idea is to allocate a multi-variate polynomial to each mesh element and consequently, the collection of such polynomials over all mesh elements will define a piecewise polynomial...

The double focal transformation and its application to data reconstruction

Many seismic data processing and imaging processes require densely and regularly sampled data, whereas the actual measurements are mostly irregularly and sparsely sampled. Therefore, seismic data reconstruction methods are utilised as a pre-processing step. Within the class of transformation-based reconstruction techniques, observed seismic data...

Population Estimation Using a 3D City Model: A Multi-Scale Country-Wide Study in the Netherlands

The remote estimation of a region’s population has for decades been a key application of geographic information science in demography. Most studies have used 2D data (maps, satellite imagery) to estimate population avoiding field surveys and questionnaires. As the availability of semantic 3D city models is constantly increasing, we investigate...

Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment

A thorough review has been performed on interpolation methods to fill gaps in time-series, efficiency criteria, and uncertainty quantifications. On one hand, there are numerous available methods: interpolation, regression, autoregressive, machine learning methods, etc. On the other hand, there are many methods and criteria to estimate...

Bayesian model calibration with interpolating polynomials based on adaptively weighted leja nodes

An efficient algorithm is proposed for Bayesian model calibration, which is commonly used to estimate the model parameters of non-linear, computationally expensive models using measurement data. The approach is based on Bayesian statistics: using a prior distribution and a likelihood, the posterior distribution is obtained through application...

Data validation and data quality assessment

Once data have been recorded, data validation procedures have to be conducted to assess the quality of the data, i.e. give a confidence grade. Furthermore, gaps may occur in time series and, depending on the purposes, these can be given values by application of e.g. interpolation. Since both aspects are strongly correlated, this chapter gives an...

Multi-GNSS-Weighted Interpolated Tropospheric Delay to Improve Long-Baseline RTK Positioning

Until now, RTK (real-time kinematic) and NRTK (Network-based RTK) have been the most popular cm-level accurate positioning approaches based on Global Navigation Satellite System (GNSS) signals in real-time. The tropospheric delay is a major source of RTK errors, especially for medium and long baselines. This source of error is difficult to...

Supervised Contrastive Learning Approach for Contextual Ranking

Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models tend to be data-hungry and require large amounts of data even for fine tuning. This paper proposes a simple yet effective method to improve ranking performance on...

Numerical Methods for Ordinary Differential Equations

In this book we discuss several numerical methods for solving ordinary differential equations. We emphasize the aspects that play an important role in practical problems. We confine ourselves to ordinary differential equations with the exception of the last chapter in which we discuss the heat equation, a parabolic partial differential equation....

Blind Polynomial Regression

Fitting a polynomial to observed data is an ubiquitous task in many signal processing and machine learning tasks, such as interpolation and prediction. In that context, input and output pairs are available and the goal is to find the coefficients of the polynomial. However, in many applications, the input may be partially known or not known at...