A neural network-based framework for financial model calibration

A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN). Determining optimal values of the model parameters is formulated as training hidden neurons within a machine learning framework, based on available financial option prices. The...

Representing the Car-Following Behaviour of Adaptive Cruise Control (ACC) Systems Using Parametric Car-Following Models

The Dutch governmental organisation Rijkswaterstaat contributes to the smooth and safe flow of traffic, as both traffic jams and accidents cost society large amounts of money each day. Roads are designed for the current traffic composition. Due to the promotion of Adaptive Cruise Control (ACC) systems, utilisation of these systems is expected to...

Parametric performance analysis and energy model calibration workflow integration: A scalable approach for buildings

High efficiency paradigms and rigorous normative standards for new and existing buildings are fundamental components of sustainability and energy transitions strategies today. However, optimistic assumptions and simplifications are often considered in the design phase and, even when detailed simulation tools are used, the validation of...

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...

Multi-Source Hydrological Data Products to Monitor High Asian River Basins and Regional Water Security

This project explored the integrated use of satellite, ground observations and hydrological distributed models to support water resources assessment and monitoring in High Mountain Asia (HMA). Hydrological data products were generated taking advantage of the synergies of European and Chinese data assets and space-borne observation systems....

Identification of diseases and physiological disorders in potato via multispectral drone imagery using machine learning tools

The rapid and precise detection of diseases and plant disorders is the basis for the adequate and timely design of management strategies. Currently, there are several non-destructive alternatives that allow early detection, highlighting the use of spectral cameras attached to unmanned aerial vehicles (UAVs). The objective of this research was...

An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation

This work demonstrates the efficiency of using iterative ensemble smoothers to estimate the parameters of an SEIR model. We have extended a standard SEIR model with age-classes and compartments of sick, hospitalized, and dead. The data conditioned on are the daily numbers of accumulated deaths and the number of hospitalized. Also, it is possible...

Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data

Process-based hydrological models seek to represent the dominant hydrological processes in a catchment. However, due to unavoidable incompleteness of knowledge, the construction of “fidelius” process-based models depends largely on expert judgment. We present a systematic approach that treats models as hierarchical assemblages of hypotheses ...

Impact of dataset size on the signature-based calibration of a hydrological model

Many calibrated hydrological models are inconsistent with the behavioral functions of catchments and do not fully represent the catchments’ underlying processes despite their seemingly adequate performance, if measured by traditional statistical error metrics. Using such metrics for calibration is hindered if only short-term data are...

Morphodynamic Modelling in Tidal and Estuarine Environments: A Heuristic Investigation into Methods to Model Medium-Term Meso-scale Morphological Features

The overarching aim of this research is to contribute towards a best practice in modelling meso-scale O(10km), medium-term O(5yrs.) morphological features in Tidal & Estuarine environments. Therefore, various modelling aspects and stages are reviewed. The first pillar argues the need for numerical models in the...

Real-time train motion parameter estimation using an Unscented Kalman Filter

Train movement dynamics are usually modelled by means of Newton's second law. The resulting dynamic equation can be very precise if the parameters that it depends on are determined accurately. However, these parameters may vary in time and show wide variations, making the calibration task nontrivial and jeopardizing the performance of a broad...

Exploring the Performance of Ensemble Smoothers to Calibrate Urban Drainage Models

Urban drainage models (UDMs) are often used to manage urban flooding. However, these models generally involve many parameters to represent the underlying complex hydrodynamic processes. This results in significant challenges to achieving effective and robust model calibration especially with frequently limited observations, leading to...

Bayesian system identification for structures considering spatial and temporal correlation

The decreasing cost and improved sensor and monitoring system technology (e.g., fiber optics and strain gauges) have led to more measurements in close proximity to each other. When using such spatially dense measurement data in Bayesian system identification strategies, the correlation in the model prediction error can become significant. The...

A Literature Review on Train Motion Model Calibration

The dynamics of a moving train are usually described by means of a motion model based on Newton's second law. This model uses as input track geometry data and train characteristics like mass, the parameters that model the running resistance, the maximum tractive effort and power, and the brake rates to be applied. It can reproduce and predict...

Health assessment framework of marine engines enabled by digital twins

The advancements in digital twins when combined with the use of the machine learning tools can facilitate the effective health assessment and diagnostics of safety critical systems. This study aims at developing a framework to address the health assessment of marine engines utilising digital twins based on first-principles. This framework...