Uncertain Model

Uncertain models address the challenge of making predictions and inferences when parameters or inputs are not precisely known, focusing on quantifying and managing this uncertainty. Current research emphasizes developing robust algorithms, such as branch and bound methods for regression analysis and Gaussian processes for mapping and control, to handle uncertainty in diverse applications. These advancements are crucial for improving the reliability of predictions in fields ranging from robotics and environmental modeling to high-dimensional data analysis, enabling more informed decision-making in the face of incomplete information. The development of efficient and accurate methods for handling uncertainty is a key focus, particularly in high-dimensional or real-time contexts.

Papers

August 19, 2024

Branch and Bound to Assess Stability of Regression Coefficients in Uncertain Models
Brian Knaeble, R. Mitchell Hughes, George Rudolph, Mark A. Abramson, Daniel Razo
Core Stability Model Uncertainty Regression Model Multi Channel Branch Uncertain Model

February 29, 2024

Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models
Yu Zhang, Long Wen, Xiangtong Yao, Zhenshan Bing, Linghuan Kong, Wei He, Alois Knoll
Gaussian Process Obstacle Avoidance Control Barrier Function Adaptive Learning Safety Critical Control Sparse Gaussian Process Uncertain Model

December 14, 2022

Gaussian Process Mapping of Uncertain Building Models with GMM as Prior
Qianqian Zou, Claus Brenner, Monika Sester
LiDAR Point Cloud Gaussian Mixture Model Uncertainty Map Gaussian Process Implicit Surface Uncertain Model Bayesian Generalized Kernel OctoMap

December 1, 2022

Path Planning Problem under non-probabilistic Uncertainty
Keivan Shariatmadar
Salesman Problem Path Planning Problem Quantified Uncertainty Non Probabilistic Uncertain Model

November 20, 2022

Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models
Lijing Wang, Takuya Kurihana, Aurelien Meray, Ilijana Mastilovic, Satyarth Praveen, Zexuan Xu, Milad Memarzadeh, Alexander Lavin, Haruko Wainwright
Digital Twin Climate Data Groundwater Flow Uncertain Model Solute Transport

Uncertain Model

Papers

Branch and Bound to Assess Stability of Regression Coefficients in Uncertain Models

Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models

Gaussian Process Mapping of Uncertain Building Models with GMM as Prior

Path Planning Problem under non-probabilistic Uncertainty

Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models