Virtualization of Classical Reality: Limits and Possibilities in Physical Simulation
June 12, 2023 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Francesco Sisini
arXiv ID
2306.07955
Category
cs.HC: Human-Computer Interaction
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
This study explores the virtualization of classical reality and aims to establish a clear framework to determine the limits and possibilities of virtual reality. It addresses two primary questions: whether an observer's senses can perceive a different reality through appropriate equipment, and whether it is possible to simulate a reality without the laws of physics. As virtual and augmented reality are increasingly used in various fields, it is crucial to provide well-founded responses to these inquiries. Understanding the limitations and achievability of virtual reality is essential for creating realistic environments in education, entertainment, and other domains. Additionally, considering the role of physics and scientific rigor in virtual contexts is important. The study presents a theoretical framework divided into three sections: Methods, Results, and Discussion. The Methods section explains the nature of computers and their ability to create perceived virtual reality. The Results section introduces the theoretical framework, emphasizing observable simulation and interactive simulation and highlighting their distinctions. Finally, the Discussion section builds upon the theoretical foundation to provide comprehensive insights and answers to the research questions. This study enhances our understanding of the boundaries and possibilities of virtual reality, offering concrete answers and valuable knowledge for the development and application of virtual reality in various domains.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted