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Learning to Float is a animated short made by Neiith for her thesis It is a great animation and when I discovered it's release was temporary I felt the need to save it and I don't pretend on keeping it only to myself I think every piece of art deserves to be accesed to everyone instead of being kept in someone's hard drive, so, here it is! Ferci's social media: https://www.instagram.com/neiith525/ https://twitter.com/NEIITH525 https://www.youtube.com/@ferci2142 And if you can, support her work, it is incredible! https://ko-fi.com/neiith Also, Neiith, if you see this, I would say I am sorry for reposting your work but I truly am not, it deserves to be avaiable for everyone beucase really, it is a piece of art and I can't bare to let it become lost media All rights reserved to Neiith and Ardila

Learning to Float is a animated short made by Neiith for her thesis It is a great animation and when I discovered it's release was temporary I felt the need to save it and I don't pretend on keeping it only to myself I think every piece of art deserves to be accesed to everyone instead of being kept in someone's hard drive, so, here it is! Ferci's social media: https://www.instagram.com/neiith525/ https://twitter.com/NEIITH525 https://www.youtube.com/@ferci2142 And if you can, support her work, it is incredible! https://ko-fi.com/neiith Also, Neiith, if you see this, I would say I am sorry for reposting your work but I truly am not, it deserves to be avaiable for everyone beucase really, it is a piece of art and I can't bare to let it become lost media All rights reserved to Neiith and Ardila

468 pages (large print) ; 24 cm

468 pages (large print) ; 24 cm

Developing an engineering design is resource-intensive and time consuming, particularly for the floats of a floatplane design, due to its complexity and limited testing facilities. Intelligent-based computational design (IBCD) techniques, which integrate computational design techniques and machine learning (ML) algorithms, offer a solution to reduce required testing by providing predictions. This paper proposes a deep learning (DL) based IBCD method for modeling floats' lift-to-drag coefficient ratio (CL/CD), where DL is one of the most powerful ML. The proposed method consists of two phases: hyper-parameter optimization and DL model training and evaluation. A genetic algorithm (GA) is employed in the first phase to explore complex hyper-parameter combinations efficiently. Evaluation of the predicted CL/CD of the floats using the DL model resulted in a satisfactory R-squared of 0.9329 and the lowest mean squared error (MSE) of 0,001536. These results demonstrate the ability of DL model to predict the float's performance accurately and can facilitate further design optimization. Thus, the proposed method can offer a time-efficient and cost effective solution for predicting float performance, aiding in optimizing floatplane designs and enhancing their functionalities.

Developing an engineering design is resource-intensive and time consuming, particularly for the floats of a floatplane design, due to its complexity and limited testing facilities. Intelligent-based computational design (IBCD) techniques, which integrate computational design techniques and machine learning (ML) algorithms, offer a solution to reduce required testing by providing predictions. This paper proposes a deep learning (DL) based IBCD method for modeling floats' lift-to-drag coefficient ratio (CL/CD), where DL is one of the most powerful ML. The proposed method consists of two phases: hyper-parameter optimization and DL model training and evaluation. A genetic algorithm (GA) is employed in the first phase to explore complex hyper-parameter combinations efficiently. Evaluation of the predicted CL/CD of the floats using the DL model resulted in a satisfactory R-squared of 0.9329 and the lowest mean squared error (MSE) of 0,001536. These results demonstrate the ability of DL model to predict the float's performance accurately and can facilitate further design optimization. Thus, the proposed method can offer a time-efficient and cost effective solution for predicting float performance, aiding in optimizing floatplane designs and enhancing their functionalities.

Developing an engineering design is resource-intensive and time consuming, particularly for the floats of a floatplane design, due to its complexity and limited testing facilities. Intelligent-based computational design (IBCD) techniques, which integrate computational design techniques and machine learning (ML) algorithms, offer a solution to reduce required testing by providing predictions. This paper proposes a deep learning (DL) based IBCD method for modeling floats' lift-to-drag coefficient ratio (CL/CD), where DL is one of the most powerful ML. The proposed method consists of two phases: hyper-parameter optimization and DL model training and evaluation. A genetic algorithm (GA) is employed in the first phase to explore complex hyper-parameter combinations efficiently. Evaluation of the predicted CL/CD of the floats using the DL model resulted in a satisfactory R-squared of 0.9329 and the lowest mean squared error (MSE) of 0,001536. These results demonstrate the ability of DL model to predict the float's performance accurately and can facilitate further design optimization. Thus, the proposed method can offer a time-efficient and cost effective solution for predicting float performance, aiding in optimizing floatplane designs and enhancing their functionalities.

LEARNING TO FLOAT LIMITED RELEASE! Film will go offline on Monday the 19th at NOON (EST) A young dragon arrives at a human town looking for a new home, little does he know that is easier said than done DIRECTED BY FERNANDA CIPRIANI: https://twitter.com/NEIITH525 ART PRINTS https://www.inprnt.com/gallery/neith/