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Probabilistically valid stochastic extensions of deterministic models for systems with uncertainty

Konstantinos Karydis, Ioannis Poulakakis, Jianxin Sun, Herbert G. Tanner
2015 The international journal of robotics research  
From an acceptable deterministic model, a stochastic one is generated, capable of capturing and reproducing uncertain system-environment interactions at given levels of fidelity.  ...  Models capable of capturing and reproducing the variability observed in experimental trials can be valuable for planning and control in the presence of uncertainty.  ...  FUNDING Table II PROBABILISTICALLY II VALID STOCHASTIC MODEL EXTENSIONS ALGORITHM 1.  ... 
doi:10.1177/0278364915576336 fatcat:motijrfnonaejhaa4qvyhtmegi

A Probabilistic Estimation of PV Capacity in Distribution Networks from Aggregated Net-load Data

Lewis Waswa, Munyaradzi Justice Chihota, Bernard Bekker
2021 IEEE Access  
First, it deals with the input modeling processes (stochastic expansion and aggregation), vital for probabilistic customer load profiles that are accurate models of the original data.  ...  The proposed probabilistic method outperforms a deterministic three-point estimate method, and the significance of the differences in the methods' results highlights the relevance of extensive uncertainty  ... 
doi:10.1109/access.2021.3119467 fatcat:zivfvep5rnarbkmpzd7fpkh42y

Provable Probabilistic Safety and Feasibility-Assured Control for Autonomous Vehicles using Exponential Control Barrier Functions [article]

Spencer Van Koevering, Yiwei Lyu, Wenhao Luo, John Dolan
2022 arXiv   pre-print
With the increasing need for safe control in the domain of autonomous driving, model-based safety-critical control approaches are widely used, especially Control Barrier Function (CBF)-based approaches  ...  Moreover, how to incorporate uncertainty into the eCBF-based constraints in high-relative-degree systems to account for safety remains an open challenge.  ...  Our main contributions in this paper are: 1) a novel extension of exponential Control Barrier Functionbased constraints to a probabilistic setting for stochastic system dynamics while preserving provably  ... 
arXiv:2205.03744v1 fatcat:uytptbrjeragvm43ibd7k2doce

Quantification of uncertain and variable model parameters in non-deterministic analysis [chapter]

Dirk Vandepitte, David Moens
2011 IUTAM Bookseries  
A multitude of models for non-deterministic structural analysis have been developed.  ...  In almost all cases the authors (have to) make assumptions on the non-deterministic nature of the physical quantity, especially for material properties.  ...  The same research group has set up a procedure for the experimental identification and the validation of a non-parametric probabilistic approach allowing model uncertainties and data uncertainties to be  ... 
doi:10.1007/978-94-007-0289-9_2 fatcat:mahfqbsuobarxmxgekbkevlk3i

Viewpoint: Stochastic research, management implications, and the Journal of Range Management

David Scarnecchia
2011 Journal of range management  
Increasing use of stochastic mathematics to address inherent uncertainty in natural systems has meant increasing challenges to write and evaluate the manuscripts reporting such research.  ...  The paper asserts that for research papers to be acceptable to a management science journal such as the Journal of Range Management, they should at least be mathematically appropriate, functionally valid  ...  Evaluation of Manuscripts with Stochastic Elements Authors should explain the reasons for use of probabilistic or stochastic elements in their models, and in papers describing their research.  ... 
doi:10.2458/azu_jrm_v57i1_scarnecchia3 fatcat:b222j5llmfbmxfmu6qvwn3yfry

An Overview of the IAEA Safety Series on Procedures for Evaluating the Reliability of Predictions Made by Environmental Transfer Models [chapter]

F. Owen Hoffman, Eduard Hofer
1988 Reliability of Radioactive Transfer Models  
CONCLUSIONS The IAEA document recognizes that models, at the very best, are only approximations of real systems; therefore, truly "valid" models do not exist.  ...  In practice, deterministic as well as probabilistic answers can be determined only imprecisely because of Type B uncertainties.  ... 
doi:10.1007/978-94-009-1369-1_1 fatcat:kf3bz5vrcnajzaoqkqatrjntcy

Bayesian Layers: A Module for Neural Network Uncertainty [article]

Dustin Tran and Michael W. Dusenberry and Mark van der Wilk and Danijar Hafner
2019 arXiv   pre-print
This enables composition via a unified abstraction over deterministic and stochastic functions and allows for scalability via the underlying system.  ...  Finally, we show how Bayesian Layers can be used within the Edward2 probabilistic programming language for probabilistic programs with stochastic processes.  ...  In principle, this lets us fit probabilistic models at many orders of magnitude larger than state of the art.  ... 
arXiv:1812.03973v3 fatcat:oxsckegvezcfljz25nlz4cfn54

Short-Term Reservoir Optimization for Flood Mitigation under Meteorological and Hydrological Forecast Uncertainty

Dirk Schwanenberg, Fernando Mainardi Fan, Steffi Naumann, Julio Issao Kuwajima, Rodolfo Alvarado Montero, Alberto Assis dos Reis
2015 Water resources management  
A lead time performance assessment of the deterministic and probabilistic ECMWF forecasts as model forcing indicate the superiority of the probabilistic model.  ...  Third, the stochastic optimization permits to introduce advanced chance constraints for refining the system operation.  ...  Check for an application of the framework to the Federal Columbia River Power System in the USA.  ... 
doi:10.1007/s11269-014-0899-1 fatcat:hx27twvwubhfvlbh7wl5wiqqsu

The relationship among probabilistic, deterministic and potential skills in predicting the ENSO for the past 161 years

Ting Liu, Youmin Tang, Dejian Yang, Yanjie Cheng, Xunshu Song, Zhaolu Hou, Zheqi Shen, Yanqiu Gao, Yanling Wu, Xiaojing Li, Banglin Zhang
2019 Climate Dynamics  
The potential predictability is considered to be a good indicator for the actual prediction skill in terms of both the deterministic measures and the probabilistic framework.  ...  First, a nonlinear monotonic relationship between the deterministic prediction skill and the probabilistic prediction skill, derived by theoretical analysis, was examined and validated using the ensemble  ...  First, we constructed the ensemble forecast system with the combination of the optimal perturbations of the initial condition and the model stochastic physical processes.  ... 
doi:10.1007/s00382-019-04967-y fatcat:wuawrbttkvaz7dmjdvmc3y2iue

A Backward SDE Method for Uncertainty Quantification in Deep Learning [article]

Richard Archibald, Feng Bao, Yanzhao Cao, He Zhang
2021 arXiv   pre-print
Numerical experiments for applications of stochastic neural networks are carried out to validate the effectiveness of our methodology.  ...  We develop a probabilistic machine learning method, which formulates a class of stochastic neural networks by a stochastic optimal control problem.  ...  Since the uncertainties in probabilistic learning and stochastic behaviors of the SNN model are described by the stochastic integral in (2), we let both θ and σ be deterministic control processes in this  ... 
arXiv:2011.14145v2 fatcat:ljnazw2to5e7xotyxw7xosr7fy

Fuzzy Simheuristics: Solving Optimization Problems under Stochastic and Uncertainty Scenarios

Diego Oliva, Pedro Copado, Salvador Hinojosa, Javier Panadero, Daniel Riera, Angel A. Juan
2020 Mathematics  
scenario, which includes uncertainty elements of both stochastic and non-stochastic nature.  ...  Simheuristics combine metaheuristics with simulation in order to solve the optimization problems with stochastic elements.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/math8122240 fatcat:4mgobabuxfcjtnr2opbza4yb3q

Why Simheuristics? Benefits, Limitations, and Best Practices When Combining Metaheuristics with Simulation

Manuel Chica, Angel A. Juan PPrez
2017 Social Science Research Network  
However, metaheuristic algorithms usually assume deterministic inputs and constraints, and thus end up solving oversimplified models of the real system being considered, casting doubt on validity and even  ...  Real-world problems are also distinguished by high levels of dynamism and uncertainty, which affect the formulation of the optimization model, its input data, and constraints.  ...  Most of these approaches are extensions of extant optimization models, and they can be classified as deterministic (i.e., based on a set of plausible scenarios), probabilistic (i.e., assuming a given probabilistic  ... 
doi:10.2139/ssrn.2919208 fatcat:ttanu6lsqfd7piqdzkgjp77nhm

Optimal, Robust Predictive Control of Nonlinear Systems under Probabilistic Uncertainty using Particles

Lars Blackmore, Brian C. Williams
2007 American Control Conference (ACC)  
In this paper we present a novel method for robust, optimal control of nonlinear systems under probabilistic uncertainty.  ...  The method extends a previous approach for linear systems that approximates the distribution of the predicted system state using a finite number of particles.  ...  This uncertainty arises due to uncertain state estimation, disturbances and modeling errors, which can be described using probabilistic models of uncertainty.  ... 
doi:10.1109/acc.2007.4282699 dblp:conf/acc/BlackmoreW07 fatcat:vupvswykrrfolfzxaz6cjmgv6e

SReach: A Probabilistic Bounded Delta-Reachability Analyzer for Stochastic Hybrid Systems [chapter]

Qinsi Wang, Paolo Zuliani, Soonho Kong, Sicun Gao, Edmund M. Clarke
2015 Lecture Notes in Computer Science  
In this paper, we present a new tool SReach, which solves probabilistic bounded reachability problems for two classes of models of stochastic hybrid systems.  ...  We demonstrate SReach's applicability by discussing three representative biological models and additional benchmarks for nonlinear hybrid systems with multiple probabilistic system parameters.  ...  For SHSs with both stochastic and non-deterministic behavior, the problem results in general in a range of probabilities, thereby becoming an optimization problem.  ... 
doi:10.1007/978-3-319-23401-4_3 fatcat:aqq6jybctndvhhieror7xgo6ru

Probability and Materials: from Nano- to Macro-Scale: A summary

L.L. Graham-Brady, S.R. Arwade, D.J. Corr, M.A. Gutiérrez, D. Breysse, M. Grigoriu, N. Zabaras
2006 Probabilistic Engineering Mechanics  
The goal of this workshop was to bring together a diverse multi-disciplinary and multi-skilled group of researchers, all of whom have an interest in the application of probabilistic models to multi-scale  ...  A set of recommendations for important future research in probability and materials was proposed by the group and is provided at the end of this paper. (L.L.  ...  Expansion and validation of stochastic modeling techniques: In general, one needs many methods for representing uncertainty. There is not one unified method applicable to all materials problems.  ... 
doi:10.1016/j.probengmech.2005.10.005 fatcat:3hpnuylionha5pjq6uqvjto3a4
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