# A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives

arXiv: https://arxiv.org/abs/2208.12904 · HTML: https://mlsanity.com/paper/2208.12904/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 266 references that are in the map.
- First posted on arXiv 27 Aug 2022.

## Builds on (most cited first, 20 of 20)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [Accurate Uncertainties for Deep Learning Using Calibrated Regression](https://mlsanity.com/paper/1807.00263/) (2018)
- [Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness](https://mlsanity.com/paper/2006.10108/) (2020)
- [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](https://mlsanity.com/paper/1512.05287/) (2015)
- [Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference](https://mlsanity.com/paper/1506.02158/) (2015)
- [Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning](https://mlsanity.com/paper/2106.04015/) (2021)
- [A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale](https://mlsanity.com/paper/2012.05841/) (2020)
- [Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic](https://mlsanity.com/paper/1404.2334/) (2014)
- [Challenges in Markov chain Monte Carlo for Bayesian neural networks](https://mlsanity.com/paper/1910.06539/) (2019)
- [Distance-based Confidence Score for Neural Network Classifiers](https://mlsanity.com/paper/1709.09844/) (2017)
- [A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies](https://mlsanity.com/paper/2208.14197/) (2022)
- [Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning](https://mlsanity.com/paper/2110.15335/) (2021)
- [Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey](https://mlsanity.com/paper/1501.00644/) (2015)
- [Continuous calibration of a digital twin: comparison of particle filter and Bayesian calibration approaches](https://mlsanity.com/paper/2011.09810/) (2020)
- [Framework to model virtual factories: a digital twin view](https://mlsanity.com/paper/2104.03034/) (2021)
- [Multiattribute Auctions Based on Generalized Additive Independence](https://mlsanity.com/paper/1401.3844/) (2014)

## Built on by (most cited first, 18 of 18)

- [Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial](https://mlsanity.com/paper/2305.04933/) (2023)
- [A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems](https://mlsanity.com/paper/2212.01351/) (2022)
- [Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Prediction](https://mlsanity.com/paper/2210.03067/) (2022)
- [Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization](https://mlsanity.com/paper/2402.17718/) (2024)
- [Nonlinear Assimilation via Score-based Sequential Langevin Sampling](https://mlsanity.com/paper/2411.13443/) (2024)
- [LLM-Based Agentic Negotiation for 6G: Addressing Uncertainty Neglect and Tail-Event Risk](https://mlsanity.com/paper/2511.19175/) (2025)
- [A framework for assuring the accuracy and fidelity of an AI-enabled Digital Twin of en route UK airspace](https://mlsanity.com/paper/2601.03120/) (2026)
- [Surrogates for Physics-based and Data-driven Modelling of Parametric Systems: Review and New Perspectives](https://mlsanity.com/paper/2603.12870/) (2026)
- [Uncertainty-aware deep learning for digital twin-driven monitoring: Application to fault detection in power lines](https://mlsanity.com/paper/2303.10954/) (2023)
- [Surrogate modeling for stochastic crack growth processes in structural health monitoring applications](https://mlsanity.com/paper/2310.07241/) (2023)
- [Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry](https://mlsanity.com/paper/2311.12755/) (2023)
- [A new perspective on Bayesian Operational Modal Analysis](https://mlsanity.com/paper/2408.08664/) (2024)
- [Digital Twin-based Control Co-Design of Full Vehicle Active Suspensions via Deep Reinforcement Learning](https://mlsanity.com/paper/2512.03891/) (2025)
- [Quantifying and Attributing Submodel Uncertainty in Stochastic Simulation Models and Digital Twins](https://mlsanity.com/paper/2602.16099/) (2026)
- [Generative Flow Networks for Model Adaptation in Digital Twins of Natural Systems](https://mlsanity.com/paper/2604.20707/) (2026)
- [Spec2Twin-Chain: Orchestrating Bi-Level Optimization with LLMs for Blockchain Digital Twin Construction](https://mlsanity.com/paper/2608.30050/) (2026)
- [Grounding Time-Series Foundation Models in Digital Twin Topology for Predictive Maintenance](https://mlsanity.com/paper/2609.40071/) (2026)
- [Towards Enriching Design-Time Models with Evidence from Simulation and Operational Activities](https://mlsanity.com/paper/2610.13456/) (2026)

## Abstract (arXiv)

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This second paper presents a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantification, optimization methods, open source datasets and tools, major findings, challenges, and future directions. Discussions focus on current methods of uncertainty quantification and optimization and how they are applied in different dimensions of a digital twin. Additionally, this paper presents a case study where a battery digital twin is constructed and tested to illustrate some of the modeling and twinning methods reviewed in this two-part review. Code and preprocessed data for generating all the results and figures presented in the case study are available on GitHub.