Boosting the Model Discovery of Hybrid Dynamical Systems in An Informed Sparse Regression Approach

2021 ◽  
Author(s):  
Nico Novelli ◽  
Stefano Lenci ◽  
Pierpaolo Belardinelli
Keyword(s):  
Dynamical Systems ◽  
Hybrid Systems ◽  
Vector Fields ◽  
A Priori ◽  
Data Driven ◽  
Computationally Efficient ◽  
Governing Equations ◽  
Sparse Regression ◽  
Discontinuity Surfaces ◽  
Efficient Data

Abstract We present an efficient data-driven sparse identification of dynamical systems. The work aims at reconstructing the different sets of governing equations and identifying discontinuity surfaces in hybrid systems when the number of discontinuities is known a priori. In a two-stages approach, we first locate the switches between separate vector fields. Then, the dynamics among the manifolds are regressed, in this case by making use of the existing algorithm of Brunton et al. [1]. The reconstruction of the discontinuity surfaces comes as the outcome of a statistical analysis implemented via symbolic regression with small clusters (micro-clusters) and a rigid library of models. These allow to classify all the feasible discontinuities that are clustered and to reduce them into the actual discontinuity surfaces. The performances of the sparse regression hybrid model discovery are tested on two numerical examples, namely, a canonical spring-mass hopper and a free/impact electromagnetic energy harvester, engineering archetypes characterized by the presence of a single and double discontinuity, respectively. Results show that a supervised approach, i.e. where the number of discontinuities is preassigned, is computationally efficient and it determines accurately both discontinuities and set of governing equations. A large improvement in the time of computation is found with the maximum achievable reliability. Informed regression-based identification offers the prospect to outperform existing data-driven identification approaches for hybrid systems at the expense of instructing the algorithm for expected discontinuities.

Boosting the Model Discovery of Hybrid Dynamical Systems in an Informed Sparse Regression Approach

2021 ◽  
Author(s):  
Nico Novelli ◽  
Stefano Lenci ◽  
Pierpaolo Belardinelli
Keyword(s):  
Dynamical Systems ◽  
Hybrid Systems ◽  
Vector Fields ◽  
A Priori ◽  
Data Driven ◽  
Governing Equations ◽  
Discontinuity Surfaces ◽  
Efficient Data ◽  
The Many ◽  
Small Clusters

Abstract We present an efficient data-driven sparse identification of dynamical systems. The work aims at reconstructing the different sets of governing equations and identify discontinuity surfaces in hybrid systems when the number of discontinuities is known a priori. In our approach, we first focus to identify the switches between the separate vector fields. Then, the dynamics among the manifolds are regressed by making use of the model discovery algorithm of Brunton et al. [1]. The reconstruction of the discontinuity surfaces comes as the outcome of a statistical analysis implemented via symbolic regression with small clusters (micro-clusters) and a rigid library of models. This allows to identify all the many possible switch points that are clustered to determine the actual discontinuity surfaces. The performances of the method are tested on two numerical examples, namely, a canonical spring–mass hopper and a free/impact electromagnetic energy harvester. These applications are characterized by the presence of a single and double discontinuity, respectively. The analyses demonstrate that in the supervised approach, i.e. where the number of discontinuities is preassigned, we are capable to determine accurately both discontinuities and set of governing equations. It is found a great improvement in time of computation reaching the maximum achievable reliability that outperform existing data-driven identification approaches for hybrid systems.

scholarly journals An Ordinal Model of Risk Based on Mariner's Judgement

2016 ◽  
Vol 70 (2) ◽  
pp. 309-324 ◽  
Author(s):  
Adan Lopez-Santander ◽  
Jonathan Lawry
Keyword(s):  
Risk Model ◽  
Risk Estimation ◽  
Ordered Probit ◽  
Data Driven ◽  
Questionnaire Data ◽  
Probability Vector ◽  
Computationally Efficient ◽  
Risk Probability ◽  
Efficient Data ◽  
Ordered Probit Models

This paper describes a statistical method for learning and estimating the risk posed by other craft in the vicinity of a vessel and an overview of its possible spatial application, simulating how professional mariners perceive and assess such risk and using navigational data obtained from a standard integrated bridge. We propose a non-linear model for risk estimation which attempts to capture mariners' judgement. Questionnaire data has been collected that captures and quantifies mariners’ judgements of risk for craft in the vicinity, where each craft is described by measurements that can be obtained easily from the data already present in the ship's navigational equipment. The dataset has then been used for analysis, training and validating Ordered Probit models in order to obtain a computationally efficient data driven model for estimating the risk probability vector posed by other craft. Finally, we discuss how this risk model can be incorporated into decision making and path finding algorithms.

scholarly journals Classification of Ventricular Arrhythmias Based on a Data Driven Approach

2021 ◽  
Author(s):  
Matthew Hotradat
Keyword(s):  
Real Time ◽  
Ventricular Arrhythmias ◽  
Data Driven ◽  
Term Therapy ◽  
Computationally Efficient ◽  
Therapy Options ◽  
Data Driven Approach ◽  
Efficient Data ◽  
Long Term Therapy ◽  
Over Time

Ventricular arrhythmias (VA) are dangerous pathophysiological conditions affecting the heart which evolve over time resulting in different manifestations such as ventricular tachycardia (VT), organized VF (OVF), and disorganized VF (DVF). Success of resuscitation for patients is greatly impacted by the type of VA and swift administration of appropriate therapy options. This thesis attempts to arrive at computationally efficient, data driven approaches for classifying and tracking VAs over time for two purposes: (1) ‘in-hospital’ scenarios for planning long-term therapy options, and (2) ‘out-of-hospital’ scenarios for tracking progression/segregation of VAs in near real-time. Using a database of 61 60-s ECG VA segments, maximum classification accuracies of 96.7% (AUC=0.993) and 87% (AUC=0.968) were achieved for VT vs. VF and OVF vs. DVF classification for ‘in-hospital’/offline analysis. Two near real-time approaches were also developed for ‘out-of-hospital’ VA incidents with results demonstrating the high potential to track VA progression and segregation over time.

scholarly journals Classification of Ventricular Arrhythmias Based on a Data Driven Approach

2021 ◽  
Author(s):  
Matthew Hotradat
Keyword(s):  
Real Time ◽  
Ventricular Arrhythmias ◽  
Data Driven ◽  
Term Therapy ◽  
Computationally Efficient ◽  
Therapy Options ◽  
Data Driven Approach ◽  
Efficient Data ◽  
Long Term Therapy ◽  
Over Time

Ventricular arrhythmias (VA) are dangerous pathophysiological conditions affecting the heart which evolve over time resulting in different manifestations such as ventricular tachycardia (VT), organized VF (OVF), and disorganized VF (DVF). Success of resuscitation for patients is greatly impacted by the type of VA and swift administration of appropriate therapy options. This thesis attempts to arrive at computationally efficient, data driven approaches for classifying and tracking VAs over time for two purposes: (1) ‘in-hospital’ scenarios for planning long-term therapy options, and (2) ‘out-of-hospital’ scenarios for tracking progression/segregation of VAs in near real-time. Using a database of 61 60-s ECG VA segments, maximum classification accuracies of 96.7% (AUC=0.993) and 87% (AUC=0.968) were achieved for VT vs. VF and OVF vs. DVF classification for ‘in-hospital’/offline analysis. Two near real-time approaches were also developed for ‘out-of-hospital’ VA incidents with results demonstrating the high potential to track VA progression and segregation over time.

scholarly journals Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers

2021 ◽  
Author(s):  
Laure Fournier ◽  
Lena Costaridou ◽  
Luc Bidaut ◽  
Nicolas Michoux ◽  
Frederic E. Lecouvet ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Quantitative Imaging ◽  
A Priori ◽  
External Validation ◽  
Data Driven ◽  
Imaging Biomarkers ◽  
Clinical Validation ◽  
Biological Processes ◽  
Imaging Data ◽  
Selection Of

Abstract Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

scholarly journals Quantifying the Impact of a Tsunami on Data-Driven Earthquake Relief Zone Planning in Los Angeles County via Multivariate Spatial Optimization

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 99 ◽  
Author(s):  
Yueqi Gu ◽  
Orhun Aydin ◽  
Jacqueline Sosa
Keyword(s):  
Los Angeles ◽  
A Priori ◽  
Safety Data ◽  
Spatial Optimization ◽  
Optimal Number ◽  
Multidisciplinary Optimization ◽  
Data Driven ◽  
Fault System ◽  
Design Data ◽  
The Impact

Post-earthquake relief zone planning is a multidisciplinary optimization problem, which required delineating zones that seek to minimize the loss of life and property. In this study, we offer an end-to-end workflow to define relief zone suitability and equitable relief service zones for Los Angeles (LA) County. In particular, we address the impact of a tsunami in the study due to LA’s high spatial complexities in terms of clustering of population along the coastline, and a complicated inland fault system. We design data-driven earthquake relief zones with a wide variety of inputs, including geological features, population, and public safety. Data-driven zones were generated by solving the p-median problem with the Teitz–Bart algorithm without any a priori knowledge of optimal relief zones. We define the metrics to determine the optimal number of relief zones as a part of the proposed workflow. Finally, we measure the impacts of a tsunami in LA County by comparing data-driven relief zone maps for a case with a tsunami and a case without a tsunami. Our results show that the impact of the tsunami on the relief zones can extend up to 160 km inland from the study area.

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