118 research outputs found
A Swin-Transformer-based Model for Efficient Compression of Turbulent Flow Data
This study proposes a novel deep-learning-based method for generating reduced
representations of turbulent flows that ensures efficient storage and transfer
while maintaining high accuracy during decompression. A Swin-Transformer
network combined with a physical constraints-based loss function is utilized to
compress the turbulent flows with high compression ratios and then restore the
data with the underlying physical properties. The forced isotropic turbulent
flow is used to demonstrate the ability of the Swin-Transformer-based (ST)
model, where the instantaneous and statistical results show the excellent
ability of the model to recover the flow data with remarkable accuracy.
Furthermore, the capability of the ST model is compared with a typical
Convolutional Neural Network-based auto-encoder (CNN-AE) by using the turbulent
channel flow at two friction Reynolds numbers = 180 and 550. The
results generated by the ST model are significantly more consistent with the
DNS data than those recovered by the CNN-AE, indicating the superior ability of
the ST model to compress and restore the turbulent flow. This study also
compares the compression performance of the ST model at different compression
ratios (CR) and finds that the model has low enough error even at very high CR.
Additionally, the effect of transfer learning (TL) is investigated, showing
that TL reduces the training time by 64\% while maintaining high accuracy. The
results illustrate for the first time that the Swin-Transformer-based model
incorporating a physically constrained loss function can compress and restore
turbulent flows with the correct physics.Comment: 21 page, 16 figure
Automated compositions: artificial intelligence aided conceptual design explorations in architecture
The paper focuses on the challenges of the relationship between architecture and artificial intelligence
(AI), in particular, the potential of this technology to support architects' creative design processes in the
form of augmented intelligence. Conceptual architectural design is an intricate process that produces
new concepts by using prior knowledge, experience, intuition, and creativity. Artificial intelligence should
not be used during the conceptual design stage with the goal of solving a problem in a predefined search
space. Instead, potential solutions and design requirements should be explored using this technology.
The suggested approach avoids preconceived solutions and psychological inertia attributed to the designer's finite experience. The paper gives a brief critical review of the application of AI in an architectural context, especially in conceptual design. The review emphasizes changes in the design process brought about by cutting-edge strategies, methods, and tools based on AI. Further, research by design is presented to illustrate the possible use of biologically inspired algorithms in producing innovative design proposals. The applied strategy in architectural design production was based on the human-machine interface and interaction (HMI&I). Symbiotic human-machine interaction in the design process facilitates the emergence of automated compositions that display novel and unexpected forms, detail, materiality, structure, functionality, and aesthetics. This study allowed us to explore peculiar architectural design methodologies and the potential of digitally intelligent architecture
Machine Learning and Its Application to Reacting Flows
This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation
Machine Learning and Its Application to Reacting Flows
This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation
Proceedings of the 2021 DigitalFUTURES
This open access book is a compilation of selected papers from 2021 DigitalFUTURES—The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021). The work focuses on novel techniques for computational design and robotic fabrication. The contents make valuable contributions to academic researchers, designers, and engineers in the industry. As well, readers encounter new ideas about understanding material intelligence in architecture
Visualization of water accumulation in micro porous layers in polymer electrolyte membrane fuel cells using synchrotron phase contrast tomography
Using phase contracted synchrotron X ray tomography, this study investigates the water distribution within the microporous layer MPL of polymer electrolyte membrane fuel cells PEMFCs . Synchrotron X ray tomography used to analyze the water distribution in the whole gas diffusion medium GDM , which comprises the microporous layer MPL and the gas diffusion layer GDL . The MPL has already been identified. In the future, the development of GDMs could be employed to enhance the performance and operating conditions of PEMFC
Simulation Intelligence: Towards a New Generation of Scientific Methods
The original "Seven Motifs" set forth a roadmap of essential methods for the
field of scientific computing, where a motif is an algorithmic method that
captures a pattern of computation and data movement. We present the "Nine
Motifs of Simulation Intelligence", a roadmap for the development and
integration of the essential algorithms necessary for a merger of scientific
computing, scientific simulation, and artificial intelligence. We call this
merger simulation intelligence (SI), for short. We argue the motifs of
simulation intelligence are interconnected and interdependent, much like the
components within the layers of an operating system. Using this metaphor, we
explore the nature of each layer of the simulation intelligence operating
system stack (SI-stack) and the motifs therein: (1) Multi-physics and
multi-scale modeling; (2) Surrogate modeling and emulation; (3)
Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based
modeling; (6) Probabilistic programming; (7) Differentiable programming; (8)
Open-ended optimization; (9) Machine programming. We believe coordinated
efforts between motifs offers immense opportunity to accelerate scientific
discovery, from solving inverse problems in synthetic biology and climate
science, to directing nuclear energy experiments and predicting emergent
behavior in socioeconomic settings. We elaborate on each layer of the SI-stack,
detailing the state-of-art methods, presenting examples to highlight challenges
and opportunities, and advocating for specific ways to advance the motifs and
the synergies from their combinations. Advancing and integrating these
technologies can enable a robust and efficient hypothesis-simulation-analysis
type of scientific method, which we introduce with several use-cases for
human-machine teaming and automated science
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