What is physical obsolescence?4 answersPhysical obsolescence refers to the declining performance or usefulness of a physical object, such as buildings or durable goods, over time. It is a serious threat to the value, usefulness, and lifespan of these objects. Physical obsolescence can be caused by factors such as wear and tear, technological advancements, or changes in market needs and expectations. The process of physical obsolescence can lead to a decrease in the value and efficiency of the object, making it outdated or no longer useful. The timing of adoption and innovation can also be affected by physical obsolescence, as decisions about when to adopt new technologies or upgrade existing ones become non-trivial.
Where have physical informed neural networks already been applied?4 answersPhysics-informed neural networks (PINNs) have been applied in various fields such as parametric partial differential equations (Bergel), applied mathematics and simulations (Context_2), thermo-poro-mechanical behavior of shear bands in landslides (Moeineddin et al.), prediction of thermophysical properties of organic compounds (Babaei and Hedengren), and electromagnetism for solving direct problems (Context_5). PINNs have been used to overcome challenges such as computational expense, missing data, and the need for additional training for each new system parameter set. They incorporate physics knowledge into the neural network training, allowing for faster training and more accurate predictions. PINNs have been successfully applied in optimal control frameworks, stability estimation of landslides, prediction of surface tension and normal boiling point, and solving inverse problems.
Why information is physical?4 answersInformation is considered physical because it is composed of physical representation, abstract meaning, and rules that connect representation to meaning. However, it is important to distinguish between the physical matter that represents information and the matter that carries information. According to the general theory of information (GTI), information itself is not physical, but it can have physical and/or mental representations. The physical structure that represents information may have mass, but the information itself does not. The precise definition of information involves selecting from a set of possibilities, and this selection can be measured and defined beforehand. Additionally, the access to or comparison with the domain of information is more frequent when it was defined earlier, suggesting a connection between information and the order of its definition along time. In summary, while information can have physical representations, the essence of information itself is non-physical.
What are the advantages and disadvantages of using algebraic inverse kinematics?3 answersAlgebraic inverse kinematics has several advantages and disadvantages. One advantage is that it allows for the efficient motion solving of different parallel robots and is convenient for kinematic control development. Another advantage is that it provides a general and concise formulation of the kinematic equation, making it independent of experience and observation. Additionally, algebraic inverse kinematics can solve all the joint variables under a given target pose simultaneously, enabling easy programming and automation of the inverse kinematic analysis. However, a disadvantage is that closed-form solutions cannot be easily obtained for the difficult nonlinear problem of inverse kinematics for serial kinematic chains. This limitation necessitates the use of computationally efficient numerical methods, such as convex optimization techniques, which may require additional computational resources.
What are the challenges in solving inverse problems with deep learning?5 answersInverse problems in deep learning face several challenges. One major challenge is the sensitivity of neural networks to noise and perturbations, which can lead to instability and poor reconstructions. Another challenge is that neural networks often do not consider the numerical formulation of the underlying problem when trained end-to-end. Additionally, the ill-posed nature of inverse problems, where the solution is difficult to approximate when noise affects the data, poses a challenge for deep learning-based methods. Furthermore, the lack of prior understanding of physics concepts required for curvature-driven regularities in variational problems makes numerical solutions challenging. These challenges highlight the need for strategies to improve stability, reduce noise amplification, and incorporate mathematical analysis and variational models in deep learning-based approaches for solving inverse problems.
What is inverse regression?5 answersInverse regression is a method used in regression analysis to predict a response variable based on a given set of predictor variables. It involves estimating the relationship between the response variable and the predictors by finding linear combinations of the predictors that capture the most relevant information about the response. This dimension reduction technique is particularly useful in high-dimensional data settings, where the number of predictors exceeds the number of observations. Inverse regression can be applied to both univariate and multivariate regression problems, and it has been shown to provide reliable and efficient estimates of the response variable. Theoretical results have been derived for inverse regression in the linear model, and recent research has focused on generalizing these results to various estimators in the high-dimensional context.