Parameter Uncertainty

Parameter uncertainty, the quantification and management of uncertainty in model parameters, is a crucial challenge across numerous scientific and engineering disciplines. Current research focuses on developing robust methods for estimating and incorporating parameter uncertainty into models, employing techniques like Bayesian inference, optimal transport, and global sensitivity analysis, often within the context of neural networks, Bayesian networks, or other advanced model architectures. Addressing parameter uncertainty is vital for improving the reliability and safety of predictions in diverse applications, ranging from autonomous driving and robotics to epidemiological modeling and materials science. This leads to more accurate predictions and better decision-making in high-stakes scenarios.

Papers

June 10, 2024

A local squared Wasserstein-2 method for efficient reconstruction of models with uncertainty
Mingtao Xia, Qijing Shen
Full Model Uncertainty Quantification High Uncertainty Anticipation Latent Variable Latent Variable Model Parameter Uncertainty Efficient Reconstruction Wasserstein 2

June 9, 2024

Global Sensitivity Analysis of Uncertain Parameters in Bayesian Networks
Rafael Ballester-Ripoll, Manuele Leonelli
Bayesian Network Sensitivity Analysis Parameter Uncertainty Global Sensitivity Analysis Global Sensitivity

April 12, 2024

Combining Statistical Depth and Fermat Distance for Uncertainty Quantification
Hai-Vy Nguyen, Fabrice Gamboa, Reda Chhaibi, Sixin Zhang, Serge Gratton, Thierry Giaccone
Neural Network Uncertainty Quantification Depth Estimation Feature Space Parameter Uncertainty Select and Combine Better Uncertainty Estimation Depth Function Fermat Distance

February 2, 2024

Parameter uncertainties for imperfect surrogate models in the low-noise regime
Thomas D Swinburne, Danny Perez
Surrogate Model Generalization Error Posterior Distribution Parameter Uncertainty Surrogate Modelling Bayesian Regression

January 23, 2024

Sparse identification of nonlinear dynamics in the presence of library and system uncertainty
Andrew O'Brien
High Uncertainty Anticipation Dynamical System Speech Presence Nonlinear Dynamic Easy to Use Library Parameter Uncertainty Sparse Identification SINDy Algorithm System Uncertainty

December 22, 2023

Information-seeking polynomial NARX model-predictive control through expected free energy minimization
Wouter M. Kouw
Model Predictive Control Free Energy Parameter Uncertainty

November 1, 2023

Synthesis and verification of robust-adaptive safe controllers
Simin Liu, Kai S. Yun, John M. Dolan, Changliu Liu
Control Barrier Function Critical Synthesis Verification Task Safe Control Parameter Uncertainty Uncertain System Safe Controller Robust Safe

September 23, 2023

Optimizing Chance-Constrained Submodular Problems with Variable Uncertainties
Xiankun Yan, Anh Viet Do, Feng Shi, Xiaoyu Qin, Frank Neumann
Submodular Maximization Efficient Optimization Parameter Uncertainty Chance Constraint Stochastic Constraint Knapsack Constraint

September 18, 2023

Desensitization and Deception in Differential Games with Asymmetric Information
Vinodhini Comandur, Tulasi Ram Vechalapu, Venkata Ramana Makkapati, Panagiotis Tsiotras, Seth Hutchinson
Optimal Control Information Asymmetry Optimal Planning Parameter Uncertainty Differential Game Uncertain Obstacle Image Desensitization

August 3, 2023

Not All Actions Are Created Equal: Bayesian Optimal Experimental Design for Safe and Optimal Nonlinear System Identification
Parker Ewen, Gitesh Gunjal, Joey Wilson, Jinsun Liu, Challen Enninful Adu, Ram Vasudevan
Experimental Design System Identification Past Action Parameter Uncertainty Nonlinear System Identification

July 23, 2023

Robust explicit model predictive control for hybrid linear systems with parameter uncertainties
Oleg Balakhnov, Sergei Savin, Alexandr Klimchik
Predictive Control Model Predictive Control Robust Model Parameter Uncertainty Hybrid System Polynomial Zonotopes

June 30, 2023

Uncertainty Informed Optimal Resource Allocation with Gaussian Process based Bayesian Inference
Samarth Gupta, Saurabh Amin
Gaussian Process Bayesian Inference Parameter Uncertainty Compartmental Model Vaccine Allocation

June 22, 2023

Design Considerations and Robustness to Parameter Uncertainty in Wire-Wrapped Cam Mechanisms
Garrison L. H. Johnston, Andrew L. Orekhov, Nabil Simaan
Native Robustness Collaborative Robot Parameter Uncertainty Dynamic Balance Design Consideration

May 1, 2023

Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty
Aliaksandr Hubin, Geir Storvik
Full Model Variational Inference Bayesian Neural Network Model Uncertainty Structure Learning Parameter Uncertainty Scalable Variational Scalable Bayesian

March 20, 2023

Uncertainty-aware deep learning for digital twin-driven monitoring: Application to fault detection in power lines
Laya Das, Blazhe Gjorgiev, Giovanni Sansavini
Deep Learning Deep Neural Network Data Detection Digital Twin Uncertainty Aware Prediction Uncertainty Parameter Uncertainty Power Line Deep Neural Network Parameter Uncertainty Aware Model

March 14, 2023

Reachability Analysis of Neural Networks with Uncertain Parameters
Pierre-Jean Meyer
Neural Network Reachability Analysis Parameter Uncertainty Interval Bound Propagation Interval Reachability

March 7, 2023

New Perspectives on Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization
Soroosh Shafieezadeh-Abadeh, Liviu Aolaritei, Florian Dörfler, Daniel Kuhn
Computation Method New Perspective Distributionally Robust Optimization Parameter Uncertainty Robust Optimization Problem Robustification Aware Manner Transport Cost

January 11, 2023

Failure Detection for Motion Prediction of Autonomous Driving: An Uncertainty Perspective
Wenbo Shao, Yanchao Xu, Liang Peng, Jun Li, Hong Wang
Autonomous Driving Motion Prediction Model Uncertainty Uncertainty Estimation Method Parameter Uncertainty Failure Detection Motion Predictor Uncertainty Score

December 28, 2022

A Learning-Based Optimal Uncertainty Quantification Method and Its Application to Ballistic Impact Problems
Xingsheng Sun, Burigede Liu
Application Proficiency Uncertainty Quantification Parameter Uncertainty Probability Measure Uncertainty Information

December 1, 2022

Shining light on data: Geometric data analysis through quantum dynamics
Akshat Kumar, Mohan Sarovar
Raw Data Data Driven Light Work Data Manifold Parameter Uncertainty Quantum Dynamic Geometric Data Solitary Wave

Parameter Uncertainty

Papers

A local squared Wasserstein-2 method for efficient reconstruction of models with uncertainty

Global Sensitivity Analysis of Uncertain Parameters in Bayesian Networks

Combining Statistical Depth and Fermat Distance for Uncertainty Quantification

Parameter uncertainties for imperfect surrogate models in the low-noise regime

Sparse identification of nonlinear dynamics in the presence of library and system uncertainty

Information-seeking polynomial NARX model-predictive control through expected free energy minimization

Synthesis and verification of robust-adaptive safe controllers

Optimizing Chance-Constrained Submodular Problems with Variable Uncertainties

Desensitization and Deception in Differential Games with Asymmetric Information

Not All Actions Are Created Equal: Bayesian Optimal Experimental Design for Safe and Optimal Nonlinear System Identification

Robust explicit model predictive control for hybrid linear systems with parameter uncertainties

Uncertainty Informed Optimal Resource Allocation with Gaussian Process based Bayesian Inference

Design Considerations and Robustness to Parameter Uncertainty in Wire-Wrapped Cam Mechanisms

Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty

Uncertainty-aware deep learning for digital twin-driven monitoring: Application to fault detection in power lines

Reachability Analysis of Neural Networks with Uncertain Parameters

New Perspectives on Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization

Failure Detection for Motion Prediction of Autonomous Driving: An Uncertainty Perspective

A Learning-Based Optimal Uncertainty Quantification Method and Its Application to Ballistic Impact Problems

Shining light on data: Geometric data analysis through quantum dynamics