Relevant bibliographies by topics / Action prediction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Action prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Action prediction"

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Liu, Lydia T., Solon Barocas, Jon Kleinberg, and Karen Levy. "On the Actionability of Outcome Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 20 (2024): 22240–49. http://dx.doi.org/10.1609/aaai.v38i20.30229.

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Predicting future outcomes is a prevalent application of machine learning in social impact domains. Examples range from predicting student success in education to predicting disease risk in healthcare. Practitioners recognize that the ultimate goal is not just to predict but to act effectively. Increasing evidence suggests that relying on outcome predictions for downstream interventions may not have desired results. In most domains there exists a multitude of possible interventions for each individual, making the challenge of taking effective action more acute. Even when causal mechanisms conn
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Cai, Yijun, Haoxin Li, Jian-Fang Hu, and Wei-Shi Zheng. "Action Knowledge Transfer for Action Prediction with Partial Videos." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8118–25. http://dx.doi.org/10.1609/aaai.v33i01.33018118.

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Predicting action class from partially observed videos, which is known as action prediction, is an important task in computer vision field with many applications. The challenge for action prediction mainly lies in the lack of discriminative action information for the partially observed videos. To tackle this challenge, in this work, we propose to transfer action knowledge learned from fully observed videos for improving the prediction of partially observed videos. Specifically, we develop a two-stage learning framework for action knowledge transfer. At the first stage, we learn feature embeddi
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Hollinger, David, Ryan S. Pollard, Mark C. Schall, Howard Chen, and Michael Zabala. "A Hierarchical-Based Learning Approach for Multi-Action Intent Recognition." Sensors 24, no. 23 (2024): 7857. https://doi.org/10.3390/s24237857.

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Recent applications of wearable inertial measurement units (IMUs) for predicting human movement have often entailed estimating action-level (e.g., walking, running, jumping) and joint-level (e.g., ankle plantarflexion angle) motion. Although action-level or joint-level information is frequently the focus of movement intent prediction, contextual information is necessary for a more thorough approach to intent recognition. Therefore, a combination of action-level and joint-level information may offer a more comprehensive approach to predicting movement intent. In this study, we devised a novel h
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Liu, Dan, Mao Ye, and Jianwei Zhang. "Improving Action Recognition Using Sequence Prediction Learning." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (2020): 2050029. http://dx.doi.org/10.1142/s0218001420500299.

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Skeleton-based action recognition distinguishes human actions using the trajectories of skeleton joints, which can be a good representation of human behaviors. Conventional methods usually construct classifiers with hand-crafted or the learned features to recognize human actions. Different from constructing a direct action classifier for action recognition task, this paper attempts to identify human actions based on the development trends of behavior sequences. Specifically, we first utilize the memory neural network to construct action predictors for each kind of activity. These action predic
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Springer, Anne, and Wolfgang Prinz. "Action Semantics Modulate Action Prediction." Quarterly Journal of Experimental Psychology 63, no. 11 (2010): 2141–58. http://dx.doi.org/10.1080/17470211003721659.

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Liu, Xiaoli, and Jianqin Yin. "Multi-Head TrajectoryCNN: A New Multi-Task Framework for Action Prediction." Applied Sciences 12, no. 11 (2022): 5381. http://dx.doi.org/10.3390/app12115381.

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Action prediction is an important task in human activity analysis, which has many practical applications, such as human–robot interactions and autonomous driving. Action prediction often comprises two subtasks: action semantic prediction and future human motion prediction. Most of the existing works treat these subtasks separately, ignoring the correlations, leading to unsatisfying performance. By contrast, we jointly model these tasks and improve human motion predictions utilizing their action semantics. In terms of methodology, we propose a novel multi-task framework (Multi-head TrajectoryCN
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Yu, Tianyu, Cuiwei Liu, Zhuo Yan, and Xiangbin Shi. "A Multi-Task Framework for Action Prediction." Information 11, no. 3 (2020): 158. http://dx.doi.org/10.3390/info11030158.

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Predicting the categories of actions in partially observed videos is a challenging task in the computer vision field. The temporal progress of an ongoing action is of great importance for action prediction, since actions can present different characteristics at different temporal stages. To this end, we propose a novel multi-task deep forest framework, which treats temporal progress analysis as a relevant task to action prediction and takes advantage of observation ratio labels of incomplete videos during training. The proposed multi-task deep forest is a cascade structure of random forests an
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Kim, Jihwan, Miso Lee, Cheol-Ho Cho, Jihyun Lee, and Jae-Pil Heo. "Prediction-Feedback DETR for Temporal Action Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 4 (2025): 4266–74. https://doi.org/10.1609/aaai.v39i4.32448.

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Temporal Action Detection (TAD) is fundamental yet challenging for real-world video applications. Leveraging the unique benefits of transformers, various DETR-based approaches have been adopted in TAD. However, it has recently been identified that the attention collapse in self-attention causes the performance degradation of DETR for TAD. Building upon previous research, this paper newly addresses the attention collapse problem in cross-attention within DETR-based TAD methods. Moreover, our findings reveal that cross-attention exhibits patterns distinct from predictions, indicating a short-cut
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Pucci, Davide, Federico Becattini, and Alberto Del Bimbo. "Joint-Based Action Progress Prediction." Sensors 23, no. 1 (2023): 520. http://dx.doi.org/10.3390/s23010520.

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Action understanding is a fundamental computer vision branch for several applications, ranging from surveillance to robotics. Most works deal with localizing and recognizing the action in both time and space, without providing a characterization of its evolution. Recent works have addressed the prediction of action progress, which is an estimate of how far the action has advanced as it is performed. In this paper, we propose to predict action progress using a different modality compared to previous methods: body joints. Human body joints carry very precise information about human poses, which
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Rosenfeld, Ariel, and Sarit Kraus. "Predicting Human Decision-Making: From Prediction to Action." Synthesis Lectures on Artificial Intelligence and Machine Learning 12, no. 1 (2018): 1–150. http://dx.doi.org/10.2200/s00820ed1v01y201712aim036.

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Dissertations / Theses on the topic "Action prediction"

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Green, Dorota. "Predictive Eye Movements During Action Observation in Infancy : Understanding the Processes Behind Action Prediction." Doctoral thesis, Uppsala universitet, Institutionen för psykologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-230994.

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Being able to predict the goal of other people’s actions is an important aspect of our daily lives. This ability allows us to interact timely with others and adjust our behaviour appropriately. The general aim of the present thesis was to explore which processes best explain our ability to predict other people’s action goals during development. There are different theories concerning this ability. Some stress the fact that observation of others actions activate the same areas of the brain involved in our own action production, this way helping us to understand what they are doing. Other theori
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Yon, Daniel. "Sensory prediction mechanisms in action." Thesis, Birkbeck (University of London), 2018. http://bbktheses.da.ulcc.ac.uk/362/.

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When we produce an action we generate predictions about the sensory consequences that are likely to ensue. This thesis tests a series of claims about the functional contribution these predictions make to perception, the role that such predictions play in processing the reactions of others, and the range of sensory inputs that these prediction mechanisms operate over. Chapter 1 outlines the theoretical background to each of these claims, alongside the previous literature that motivates subsequent experiments. The first three empirical chapters focus on claims about the functional role of sensor
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Elsner, Claudia. "An Embodied Account of Action Prediction." Doctoral thesis, Uppsala universitet, Institutionen för psykologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-236868.

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Being able to generate predictions about what is going to happen next while observing other people’s actions plays a crucial role in our daily lives. Different theoretical explanations for the underlying processes of humans’ action prediction abilities have been suggested. Whereas an embodied account posits that predictive gaze relies on embodied simulations in the observer’s motor system, other accounts do not assume a causal role of the motor system for action prediction. The general aim of this thesis was to augment current knowledge about the functional mechanisms behind humans’ action pre
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Pesquita, Ana. "The social is predictive : human sensitivity to attention control in action prediction." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/59076.

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Observing others is predicting others. Humans have a natural tendency to make predictions about other people’s future behavior. This predisposition sits at the basis of social cognition: others become accessible to us because we are able to simulate their internal states, and in this way make predictions about their future behavior (Blakemore & Decety, 2001). In this thesis, I examine prediction in the social realm through three main contributions. The first contribution is of a theoretical nature, the second is methodological, and the third contribution is empirical. On the theoretical plane,
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Roussel, Cédric. "A preactivation theory of action effect prediction." Thesis, Paris 5, 2013. http://www.theses.fr/2013PA05H116/document.

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L’objectif du présent doctorat fut de contribuer à la compréhension des mécanismes de prédiction des effets de l’action en termes d’implémentation cérébral. Il a été suggéré que la prédiction des effets de l’action reposait sur la préactivation du réseau sensoriel impliqué dans le traitement de ces effets (voir Chapitre I.H). A partir de cette suggestion nous avons élaboré un model de cette hypothèse de preactivation nous permettant de dériver un certain nombre de prédictions quant au traitement perceptuel des conséquences de l’action. Au cours de cette thèse nous avons testé les prédictions f
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Calem, Laura. "Action and trajectory prediction for Autonomous Driving." Electronic Thesis or Diss., Paris, HESAM, 2024. http://www.theses.fr/2024HESAC011.

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Cette thèse de doctorat, dans le contexte applicatif de la conduite autonome, se concentre sur l'exploration des mécanismes favorisant la diversité dans les modèles génératifs, qui produisent une distribution probabiliste des trajectoires futures étant donné les trajectoires passées. Les ensembles de données de prévision de trajectoire ne fournissant qu'une trajectoire future pour une trajectoire passée et une disposition spatiale de scène données, de nombreuses méthodes existantes se concentrent sur la précision de la meilleure trajectoire prédite par rapport à la trajectoire future (vérité t
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AlBahar, Badour A. Sh A. "Im2Vid: Future Video Prediction for Static Image Action Recognition." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83602.

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Static image action recognition aims at identifying the action performed in a given image. Most existing static image action recognition approaches use high-level cues present in the image such as objects, object human interaction, or human pose to better capture the action performed. Unlike images, videos have temporal information that greatly improves action recognition by resolving potential ambiguity. We propose to leverage a large amount of readily available unlabeled videos to transfer the temporal information from video domain to static image domain and hence improve static image action
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Diersch, Nadine [Verfasser]. "Action prediction in the aging mind / Nadine Diersch." Leipzig : Max-Planck-Institut für Kognitions- und Neurowissenschaften, 2013. http://d-nb.info/1064760015/34.

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Rezazadegan, Fahimeh. "Human action recognition and prediction for robotics applications." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/127283/1/__qut.edu.au_Documents_StaffHome_StaffGroupH%24_halla_Desktop_Fahimeh_Rezazadegan_Thesis.pdf.

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This study is a step forward in developing two different methods; one recognises human actions in an unbiased environment, the other predicts the next human action. The proposed methods that are based on deep learning, convolutional neural networks and long-short term memories, work regardless of camera motion, viewpoint variation, and irrelevant background context. The key outcome of this research is to enable an assistive robot to help a human peer performing an assembly task, using the proposed algorithms.
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Mulligan, Desmond. "Evidence for the neuromotor simulation hypothesis in action prediction." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58027.

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The overarching aim of this thesis was to further understand the processes and internal representations involved in predicting action outcomes, by manipulating information sources during learning and prediction. Growing evidence suggests that the human motor system is activated during action observation, such that motor representations are invoked, through simulative processes that help facilitate an understanding of the unfolding action. In this work, we employed a design that manipulated visual and motor influences during learning and prediction, to try to understand; a) the types of interna
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Books on the topic "Action prediction"

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Thompson, Keith E. Reasoned action theory applied to the prediction of olive oil usage. Cranfield School of Management, 1994.

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Icek, Ajzen, ed. Predicting and changing behavior: The reasoned action approach. Psychology Press, 2010.

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Benni, Stefano. Timeskipper. Europa Editions, 2008.

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Donna, Youngs, ed. Investigative psychology: Offender profiling and the analysis of criminal action. John Wiley & Sons, 2009.

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1938-, Schwab Klaus, ed. Overcoming indifference: Ten key challenges in today's changing world : a survey of ideas and proposals for action on the threshold of the twenty-first century. New York University Press, 1995.

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Maldonato, Mauro. The predictive brain: Consciousness, decision and embodied action. Sussex Academic Press, 2014.

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Fishbein, Martin. Predicting and changing behavior: The reasoned action approach. Psychology Press, 2010.

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Fishbein, Martin. Predicting and changing behavior: The reasoned action approach. Psychology Press, 2010.

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Juang, Jer-Nan. Deadbeat predictive controllers. National Aeronautics and Space Administration, Langley Research Center, 1997.

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J, Kuhlmann, Roden D. M, and International Congress of Pharmacology (13th : 1998 : Munich, Germany), eds. Prediction of clinical drug actions from in-vitro approaches: Fact or fiction? W. Zuckschwerdt, 1999.

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Book chapters on the topic "Action prediction"

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Jia, Chengcheng, Wei Pang, and Yun Fu. "Multimodal Action Recognition." In Human Activity Recognition and Prediction. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27004-3_4.

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Schuwerk, Tobias, and Markus Paulus. "Action Prediction in Autism." In Encyclopedia of Autism Spectrum Disorders. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-6435-8_102206-1.

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Taleb, Nassim N., and Avital Pilpel. "The Prediction of Action." In A Companion to the Philosophy of Action. Wiley-Blackwell, 2010. http://dx.doi.org/10.1002/9781444323528.ch51.

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Schuwerk, Tobias, and Markus Paulus. "Action Prediction in Autism." In Encyclopedia of Autism Spectrum Disorders. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-91280-6_102206.

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Jia, Chengcheng, Yu Kong, Zhengming Ding, and Yun Fu. "RGB-D Action Recognition." In Human Activity Recognition and Prediction. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27004-3_5.

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Kong, Yu, and Yun Fu. "Action Recognition and Human Interaction." In Human Activity Recognition and Prediction. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27004-3_2.

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Jia, Chengcheng, and Yun Fu. "Subspace Learning for Action Recognition." In Human Activity Recognition and Prediction. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27004-3_3.

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Dorr, Michael, and Eleonora Vig. "Saliency Prediction for Action Recognition." In Visual Content Indexing and Retrieval with Psycho-Visual Models. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57687-9_5.

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Mulligan, Desmond, and Nicola J. Hodges. "Motor simulation in action prediction." In Anticipation and Decision Making in Sport. Routledge, 2019. http://dx.doi.org/10.4324/9781315146270-9.

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Liu, Cuiwei, Yaguang Lu, Xiangbin Shi, Zhaokui Li, and Liang Zhao. "Action Prediction Using Unsupervised Semantic Reasoning." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70090-8_50.

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Conference papers on the topic "Action prediction"

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Gold, Leon, Luke Cortez, Jared Carrillo, Bingbing Li, Edward T. Chow, and Thomas Lu. "Utilizing V-JEPA for action recognition on the lunar surface: advancing human-robot collaboration." In Pattern Recognition and Prediction XXXVI, edited by Mohammad S. Alam and Vijayan K. Asari. SPIE, 2025. https://doi.org/10.1117/12.3052411.

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Lai, Zhoukaiwen. "Driver Behavior and Action Prediction in Human-Computer Interaction." In 2024 3rd International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC). IEEE, 2024. http://dx.doi.org/10.1109/aiotc63215.2024.10748264.

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Kondapally, Anirudh, Kentaro Yamada, and Hitomi Yanaka. "Action Inference for Destination Prediction in Vision-and-Language Navigation." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-srw.26.

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Pang, Guoliang, Xionghui Wang, Jian-Fang Hu, Qing Zhang, and Wei-Shi Zheng. "DBDNet: Learning Bi-directional Dynamics for Early Action Prediction." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/126.

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Predicting future actions from observed partial videos is very challenging as the missing future is uncertain and sometimes has multiple possibilities. To obtain a reliable future estimation, a novel encoder-decoder architecture is proposed for integrating the tasks of synthesizing future motions from observed videos and reconstructing observed motions from synthesized future motions in an unified framework, which can capture the bi-directional dynamics depicted in partial videos along the temporal (past-to-future) direction and reverse chronological (future-back-to-past) direction. We then em
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Bao, Yanan, Huasen Wu, and Xin Liu. "From Prediction to Action." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016. http://dx.doi.org/10.1145/2939672.2939871.

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Lee, Dongha, Chanyoung Park, Hyunjun Ju, Junyoung Hwang, and Hwanjo Yu. "Action Space Learning for Heterogeneous User Behavior Prediction." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/392.

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Users' behaviors observed in many web-based applications are usually heterogeneous, so modeling their behaviors considering the interplay among multiple types of actions is important. However, recent collaborative filtering (CF) methods based on a metric learning approach cannot learn multiple types of user actions, because they are developed for only a single type of user actions. This paper proposes a novel metric learning method, called METAS, to jointly model heterogeneous user behaviors. Specifically, it learns two distinct spaces: 1) action space which captures the relations among all ob
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Yao, Yu, Ella Atkins, Matthew Johnson-Roberson, Ram Vasudevan, and Xiaoxiao Du. "Coupling Intent and Action for Pedestrian Crossing Behavior Prediction." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/171.

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Accurate prediction of pedestrian crossing behaviors by autonomous vehicles can significantly improve traffic safety. Existing approaches often model pedestrian behaviors using trajectories or poses but do not offer a deeper semantic interpretation of a person's actions or how actions influence a pedestrian's intention to cross in the future. In this work, we follow the neuroscience and psychological literature to define pedestrian crossing behavior as a combination of an unobserved inner will (a probabilistic representation of binary intent of crossing vs. not crossing) and a set of multi-cla
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Farias, Giovani, Ramon Fraga Pereira, Lucas Hilgert, Felipe Meneguzzi, Renata Vieira, and Rafael H. Bordini. "Failure Prediction based on Monitoring Sequences of Actions and Action Duration." In Workshop-Escola de Sistemas de Agentes, seus Ambientes e Aplicações. Sociedade Brasileira de Computação, 2016. https://doi.org/10.5753/wesaac.2016.33207.

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An agent can attempt to achieve multiple goals and each goal can be achieved by applying various different plans. Anticipating failures in agent plan execution is important to enable an agent to develop strategies to avoid or circumvent such failures, allowing the agent to achieve its goal. Plan recognition can be used to infer which plans are being executed from observations of sequences of activities being performed by an agent. Symbolic Plan Recognition is an algorithm that represents knowledge about the agents under observation in the form of a plan library. In this paper, we use this symb
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Chen, Lei, Muheng Li, Yueqi Duan, Jie Zhou, and Jiwen Lu. "Uncertainty-Aware Representation Learning for Action Segmentation." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/115.

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In this paper, we propose an uncertainty-aware representation Learning (UARL) method for action segmentation. Most existing action segmentation methods exploit continuity information of the action period to predict frame-level labels, which ignores the temporal ambiguity of the transition region between two actions. Moreover, similar periods of different actions, e.g., the beginning of some actions, will confuse the network if they are annotated with different labels, which causes spatial ambiguity. To address this, we design the UARL to exploit the transitional expression between two action p
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Fang, Fen, Qianli Xu, Nicolas Gauthier, Liyuan Li, and Joo-Hwee Lim. "Enhancing Multi-Step Action Prediction for Active Object Detection." In 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021. http://dx.doi.org/10.1109/icip42928.2021.9506078.

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Reports on the topic "Action prediction"

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Kennedy, Vincent W. Strategic Prediction for Adaptive Action: Informing the United Nations. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada326567.

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Rector, Russell H. Enemy Course of Action Prediction: Can We, Should We? Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada300713.

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Perdigão, Rui A. P. Strengthening Multi-Hazard Resilience with Quantum Aerospace Systems Intelligence. Synergistic Manifolds, 2024. http://dx.doi.org/10.46337/240301.

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The present work further enhances and deploys our Quantum Aerospace Systems Intelligence technologies (DOI: 10.46337/quasi.230901) onto Multi-Hazard risk assessment and action, from sensing and prediction to modelling, decision support and active response, towards strengthening its fundamental knowledge, awareness and resilience in the face of multi-domain challenges. Moreover, it introduces our updated post-quantum aerospace engineering ecosystem for empowering active system dynamic capabilities to mitigate or even counter multi-hazard threats from space, leveraging our high energy technologi
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ZARIPOVA, RIMMA, та OLGA ROCHEVA. РАЗРАБОТКА СИСТЕМЫ УЧЕТА КАДРОВ ДЛЯ ДОРОЖНОГО ПРЕДПРИЯТИЯ. Science and Innovation Center Publishing House, 2019. http://dx.doi.org/10.12731/2070-7568-2020-4-4-108-112.

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Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, 2021. http://dx.doi.org/10.46337/210930.

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Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platfor
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Bush, Stephen. Active Virtual Network Management Prediction. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada387998.

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Antic, Nemanj, Ameet Morjaria, and Miguel Ángel Talamas Marcos. Preliminary Findings: Task Clarity and Credibility in Relational Contracts. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0013146.

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We develop and test a relational contracting model where building relationships requires the principal and agent to solve task clarity and credibility problems. We model task clarity as the likelihood of the agent finding a productive action for the principal and demonstrate that it influences the agents propensity to fulfill promises the usual notion of credibility in relational contracts. This is because improving task clarity increases the ease of replacing a relationship after a defection, making defection more tempting. We validate our model using a decade of administrative data from the
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Saptsin, Vladimir, and Володимир Миколайович Соловйов. Relativistic quantum econophysics – new paradigms in complex systems modelling. [б.в.], 2009. http://dx.doi.org/10.31812/0564/1134.

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Abstract:
This work deals with the new, relativistic direction in quantum econophysics, within the bounds of which a change of the classical paradigms in mathematical modelling of socio-economic system is offered. Classical physics proceeds from the hypothesis that immediate values of all the physical quantities, characterizing system’s state, exist and can be accurately measured in principle. Non-relativistic quantum mechanics does not reject the existence of the immediate values of the classical physical quantities, nevertheless not each of them can be simultaneously measured (the uncertainty principle
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Seale, Maria, Natàlia Garcia-Reyero, R. Salter, and Alicia Ruvinsky. An epigenetic modeling approach for adaptive prognostics of engineered systems. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41282.

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Prognostics and health management (PHM) frameworks are widely used in engineered systems, such as manufacturing equipment, aircraft, and vehicles, to improve reliability, maintainability, and safety. Prognostic information for impending failures and remaining useful life is essential to inform decision-making by enabling cost versus risk estimates of maintenance actions. These estimates are generally provided by physics-based or data-driven models developed on historical information. Although current models provide some predictive capabilities, the ability to represent individualized dynamic f
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Haack, Jeffrey Robert. Towards Enabling Predictive Scale-Bridging Simulations through Active Learning. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1617363.

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