Digital Twins for forecasting and decision optimisation with machine learning: applications in wastewater treatment
Fuente:
arXiv
Saved in:
| Main Authors: | Colwell, Matthew, Abolghasemi, Mahdi |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Dynamical errors in machine learning forecasts
by: Fang, Zhou, et al.
Published: (2025)
by: Fang, Zhou, et al.
Published: (2025)
Current applications and potential future directions of reinforcement learning-based Digital Twins in agriculture
by: Goldenits, Georg, et al.
Published: (2024)
by: Goldenits, Georg, et al.
Published: (2024)
Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure
by: English, Lucas, et al.
Published: (2023)
by: English, Lucas, et al.
Published: (2023)
An explainable machine learning approach for energy forecasting at the household level
by: Béraud, Pauline, et al.
Published: (2024)
by: Béraud, Pauline, et al.
Published: (2024)
Coronary Artery Disease Classification Using One-dimensional Convolutional Neural Network
by: Phoemsuk, Atitaya, et al.
Published: (2024)
by: Phoemsuk, Atitaya, et al.
Published: (2024)
Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios
by: Gerhards, Ben, et al.
Published: (2025)
by: Gerhards, Ben, et al.
Published: (2025)
A feature-stable and explainable machine learning framework for trustworthy decision-making under incomplete clinical data
by: Andrys-Olek, Justyna, et al.
Published: (2026)
by: Andrys-Olek, Justyna, et al.
Published: (2026)
Predicting human decisions with behavioral theories and machine learning
by: Plonsky, Ori, et al.
Published: (2019)
by: Plonsky, Ori, et al.
Published: (2019)
Review of multimodal machine learning approaches in healthcare
by: Krones, Felix, et al.
Published: (2024)
by: Krones, Felix, et al.
Published: (2024)
Practical machine learning is learning on small samples
by: Sapir, Marina
Published: (2025)
by: Sapir, Marina
Published: (2025)
FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting
by: Zhong, Xiaohui, et al.
Published: (2024)
by: Zhong, Xiaohui, et al.
Published: (2024)
Position: Foundation Models Need Digital Twin Representations
by: Shen, Yiqing, et al.
Published: (2025)
by: Shen, Yiqing, et al.
Published: (2025)
From Transformers to Large Language Models: A systematic review of AI applications in the energy sector towards Agentic Digital Twins
by: Antonesi, Gabriel, et al.
Published: (2025)
by: Antonesi, Gabriel, et al.
Published: (2025)
A comparative study of deep learning and ensemble learning to extend the horizon of traffic forecasting
by: Zheng, Xiao, et al.
Published: (2025)
by: Zheng, Xiao, et al.
Published: (2025)
Automated machine learning: AI-driven decision making in business analytics
by: Schmitt, Marc
Published: (2022)
by: Schmitt, Marc
Published: (2022)
What should an AI assessor optimise for?
by: Romero-Alvarado, Daniel, et al.
Published: (2025)
by: Romero-Alvarado, Daniel, et al.
Published: (2025)
Learning Paradigms and Modelling Methodologies for Digital Twins in Process Industry
by: Mayr, Michael, et al.
Published: (2024)
by: Mayr, Michael, et al.
Published: (2024)
FuXi-S2S: A machine learning model that outperforms conventional global subseasonal forecast models
by: Chen, Lei, et al.
Published: (2023)
by: Chen, Lei, et al.
Published: (2023)
Exploring the design space of deep-learning-based weather forecasting systems
by: Siddiqui, Shoaib Ahmed, et al.
Published: (2024)
by: Siddiqui, Shoaib Ahmed, et al.
Published: (2024)
Recursive deep learning framework for forecasting the decadal world economic outlook
by: Wang, Tianyi, et al.
Published: (2023)
by: Wang, Tianyi, et al.
Published: (2023)
Adaptive traffic signal safety and efficiency improvement by multi objective deep reinforcement learning approach
by: Mirbakhsh, Shahin, et al.
Published: (2024)
by: Mirbakhsh, Shahin, et al.
Published: (2024)
Harmful algal bloom forecasting. A comparison between stream and batch learning
by: Molares-Ulloa, Andres, et al.
Published: (2024)
by: Molares-Ulloa, Andres, et al.
Published: (2024)
Ultra-short-term solar power forecasting by deep learning and data reconstruction
by: Wang, Jinbao, et al.
Published: (2025)
by: Wang, Jinbao, et al.
Published: (2025)
Causality in the human niche: lessons for machine learning
by: Lange, Richard D., et al.
Published: (2025)
by: Lange, Richard D., et al.
Published: (2025)
Explanatory machine learning for sequential human teaching
by: Ai, Lun, et al.
Published: (2022)
by: Ai, Lun, et al.
Published: (2022)
Joint Hypergraph Rewiring and Memory-Augmented Forecasting Techniques in Digital Twin Technology
by: Sakhinana, Sagar Srinivas, et al.
Published: (2024)
by: Sakhinana, Sagar Srinivas, et al.
Published: (2024)
Digital Twin-Driven Communication-Efficient Federated Anomaly Detection for Industrial IoT
by: Belay, Mohammed Ayalew, et al.
Published: (2026)
by: Belay, Mohammed Ayalew, et al.
Published: (2026)
Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes
by: Chung, Jaehong, et al.
Published: (2025)
by: Chung, Jaehong, et al.
Published: (2025)
Extreme value forecasting using relevance-based data augmentation with deep learning models
by: Hua, Junru, et al.
Published: (2025)
by: Hua, Junru, et al.
Published: (2025)
Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust
by: Erlygin, Leonid, et al.
Published: (2023)
by: Erlygin, Leonid, et al.
Published: (2023)
Using machine learning for fault detection in lighthouse light sensors
by: Kampouridis, Michael, et al.
Published: (2024)
by: Kampouridis, Michael, et al.
Published: (2024)
Decoding complexity: how machine learning is redefining scientific discovery
by: Vinuesa, Ricardo, et al.
Published: (2024)
by: Vinuesa, Ricardo, et al.
Published: (2024)
Operating critical machine learning models in resource constrained regimes
by: Selvan, Raghavendra, et al.
Published: (2023)
by: Selvan, Raghavendra, et al.
Published: (2023)
Predicting life satisfaction using machine learning and explainable AI
by: Khan, Alif Elham, et al.
Published: (2025)
by: Khan, Alif Elham, et al.
Published: (2025)
survex: an R package for explaining machine learning survival models
by: Spytek, Mikołaj, et al.
Published: (2023)
by: Spytek, Mikołaj, et al.
Published: (2023)
A deep learning and machine learning approach to predict neonatal death in the context of São Paulo
by: Raihan, Mohon, et al.
Published: (2025)
by: Raihan, Mohon, et al.
Published: (2025)
Data-Driven Open-Loop Simulation for Digital-Twin Operator Decision Support in Wastewater Treatment
by: Simethy, Gary, et al.
Published: (2026)
by: Simethy, Gary, et al.
Published: (2026)
Digital Twin-Assisted Explainable AI for Robust Beam Prediction in mmWave MIMO Systems
by: Khan, Nasir, et al.
Published: (2025)
by: Khan, Nasir, et al.
Published: (2025)
Length-Aware Adversarial Training for Variable-Length Trajectories: Digital Twins for Mall Shopper Paths
by: Sun, He, et al.
Published: (2026)
by: Sun, He, et al.
Published: (2026)
Shadows of quantum machine learning
by: Jerbi, Sofiene, et al.
Published: (2023)
by: Jerbi, Sofiene, et al.
Published: (2023)
Similar Items
-
Dynamical errors in machine learning forecasts
by: Fang, Zhou, et al.
Published: (2025) -
Current applications and potential future directions of reinforcement learning-based Digital Twins in agriculture
by: Goldenits, Georg, et al.
Published: (2024) -
Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure
by: English, Lucas, et al.
Published: (2023) -
An explainable machine learning approach for energy forecasting at the household level
by: Béraud, Pauline, et al.
Published: (2024) -
Coronary Artery Disease Classification Using One-dimensional Convolutional Neural Network
by: Phoemsuk, Atitaya, et al.
Published: (2024)