Deep neural network enabled corrective source term approach to hybrid analysis and modeling

Blakseth, Sindre Stenen; Rasheed, Adil; Kvamsdal, Trond; San, Omer

doi:10.1016/j.neunet.2021.11.021

Cited by 24 publications

(19 citation statements)

References 19 publications

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“…• Predictive DT: In the current project, we used either a purely physics-based model or a data-driven model to predict the external and internal state of the house, but both approaches have inherent weaknesses, as discussed in [2]. Recent works [61], [62] have shown how a hybrid modeling approach can address these weaknesses and make accurate and more certain predictions, making it ideal for modeling partially understood physics and addressing the issues of input parameter uncertainties. For instance, [63] has already shown the applicability of accurately modeling heat transfer in an aluminum extraction process, which is similar to the building energy modeling considered in our work.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Driven Digital Twin of a Modern House Demonstrated in Virtual Reality

2023

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A digital twin is a powerful tool that can help monitor and optimize physical assets in realtime. Simply put, it is a virtual representation of a physical asset, enabled through data and simulators, that can be used for a variety of purposes such as prediction, monitoring, and decision-making. However, the concept of digital twin can be vague and difficult to understand, which is why a new concept called "capability level" has been introduced. This concept categorizes digital twins based on their capability and defines a scale from zero to five, with each level indicating an increasing level of functionality. These levels are standalone, descriptive, diagnostic, predictive, prescriptive, and autonomous. By understanding the capability level of a digital twin, we can better understand its potential and limitations. To demonstrate the concepts, we use a modern house as an example. The house is equipped with a range of sensors that collect data about its internal state, which can then be used to create digital twins of different capability levels. These digital twins can be visualized in virtual reality, allowing users to interact with and manipulate the virtual environment. The current work not only presents a blueprint for developing digital twins but also suggests future research directions to enhance this technology. Digital twins have the potential to transform the way we monitor and optimize physical assets, and by understanding their capabilities, we can unlock their full potential.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Driven Digital Twin of a Modern House Demonstrated in Virtual Reality

2023

Self Cite

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“…Suppose now to endow the output manifold M n with a Riemannian metric g n , for example the Euclidean metric for a regression task. Then we can equip the other manifolds M i of the sequence (10) with a (in general singular) Riemannian metric g i via the pullback of g n through the layers Λ i , namely…”

Section: A Singular Riemannian Approach To Neural Networkmentioning

confidence: 99%

“…Neural Networks (NN) have been acknowledged to be a very powerful tool in machine learning tasks: A not fully comprehensive list of such tasks includes as speech-to-text transcription [1,2], image segmentation [3,4], image classification [5], match new items and/or products with user's interests [6], image morphing [7], imitation learning [8], solution to nonlinear PDEs [9,10], image generation [11,12]. The beginning of the new millennium has seen a growing interest in this automatic learning approach, due to the rising computational power (more performant GPUs) and the huge amount of data (the so-called Big Data Revolution).…”

Section: Introductionmentioning

confidence: 99%

A singular Riemannian geometry approach to Deep Neural Networks II. Reconstruction of 1-D equivalence classes

Benfenati¹,

Marta²

2021

Preprint

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“…Blakseth et al propose the system CoSTA [6]. They develop a hybrid strategy to complement a physics-based model with a second data-driven term that will be in charge of learning the corrections.…”

Section: Introductionmentioning

confidence: 99%

“…In [36], the authors proposed a hybrid twin of a hyperelastic beam with moving loads, displayed by means of augmented reality. In the field of perception and reasoning, Blakseth et al [6] exploits DDDAs in scene understanding in unknown scenarios. Schenck and Fox [44,45] propose a system for physical reasoning about liquids that is corrected from observations without physical priors.…”

Section: Introductionmentioning

confidence: 99%

A thermodynamics-informed active learning approach to perception and reasoning about fluids

et al. 2023

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Learning and reasoning about physical phenomena is still a challenge in robotics development, and computational sciences play a capital role in the search for accurate methods able to provide explanations for past events and rigorous forecasts of future situations. We propose a thermodynamics-informed active learning strategy for fluid perception and reasoning from observations. As a model problem, we take the sloshing phenomena of different fluids contained in a glass. Starting from full-field and high-resolution synthetic data for a particular fluid, we develop a method for the tracking (perception) and simulation (reasoning) of any previously unseen liquid whose free surface is observed with a commodity camera. This approach demonstrates the importance of physics and knowledge not only in data-driven (gray-box) modeling but also in real-physics adaptation in low-data regimes and partial observations of the dynamics. The presented method is extensible to other domains such as the development of cognitive digital twins able to learn from observation of phenomena for which they have not been trained explicitly.

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Deep neural network enabled corrective source term approach to hybrid analysis and modeling

Cited by 24 publications

References 19 publications

Artificial Intelligence-Driven Digital Twin of a Modern House Demonstrated in Virtual Reality

Artificial Intelligence-Driven Digital Twin of a Modern House Demonstrated in Virtual Reality

A singular Riemannian geometry approach to Deep Neural Networks II. Reconstruction of 1-D equivalence classes

A thermodynamics-informed active learning approach to perception and reasoning about fluids

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