scholarly journals Establishing exhaustive metasurface robustness against fabrication uncertainties through deep learning

Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ronald P. Jenkins ◽  
Sawyer D. Campbell ◽  
Douglas H. Werner
Keyword(s):  
Deep Learning ◽  
Design Process ◽  
Functional Materials ◽  
Computational Electromagnetics ◽  
Inverse Design ◽  
Design Tools ◽  
Structural Error ◽  
Significant Performance ◽  
Engineered Materials ◽  
Exhaustive Study

Abstract Photonic engineered materials have benefitted in recent years from exciting developments in computational electromagnetics and inverse-design tools. However, a commonly encountered issue is that highly performant and structurally complex functional materials found through inverse-design can lose significant performance upon being fabricated. This work introduces a method using deep learning (DL) to exhaustively analyze how structural issues affect the robustness of metasurface supercells, and we show how systems can be designed to guarantee significantly better performance. Moreover, we show that an exhaustive study of structural error is required to make strong guarantees about the performance of engineered materials. The introduction of DL into the inverse-design process makes this problem tractable, enabling optimization runtimes to be measurable in days rather than months and allowing designers to establish exhaustive metasurface robustness guarantees.

Buildings with environmental quality management, part 2: Integration of hydronic heating/cooling with thermal mass

2017 ◽  
Vol 41 (5) ◽  
pp. 397-417 ◽  
Author(s):  
A Romanska-Zapala ◽  
M Bomberg ◽  
M Fedorczak-Cisak ◽  
M Furtak ◽  
D Yarbrough ◽  
...  
Keyword(s):  
Control Systems ◽  
Design Process ◽  
Architectural Design ◽  
Integrated Design ◽  
Field Performance ◽  
Functional Materials ◽  
Heating System ◽  
Transient Temperature ◽  
Thermal Mass ◽  
Integrated Design Process

The quest for a sustainable built environment brought dramatic changes to architectural design because of the integrated design process. The integrated design process is the modern way to realize “performance architecture,” that is, design with a view to field performance. Integrated design process permits merging of concepts from passive-house designs, solar engineering, and an integration of the building enclosure with mechanical services. In part 1 of this series, the emergence of many new multi-functional materials was discussed. Yet, current innovation is guided by lessons from history. Thermal mass in heavy masonry buildings allowed periodic heating. The authors postulate integration of a hydronic heating system with the walls and the use of smart temperature control of the heating system to modify and optimize the thermal mass contribution. To use the mass of a building, one must accept transient temperature conditions where the indoor temperature varies but is confined by comfort requirements for both summer and winter conditions. On the other side, resiliency requirements dictate that in the absence of electricity the air temperature does not fall below about 12°C over a period of several hours. This requirement implies that summer cooling will likely be separated from the heating systems and that operation of a low-energy building is heavily dependent on the design of smart control systems. Analysis of control systems provided in this article for earth-to-air heat exchangers and cooling of houses with lightweight walls lead us to the requirements of separation between heating and ventilation and needs for different sources of fresh air. Finally, a new concept emerges.

Exploring Biases Between Human and Machine Generated Designs

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Christian E. Lopez ◽  
Scarlett R. Miller ◽  
Conrad S. Tucker
Keyword(s):  
Computer Simulation ◽  
Deep Learning ◽  
Domain Knowledge ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Design Tools ◽  
Generative Design ◽  
Generate Functional ◽  
Intended Function ◽  
2D Sketches

The objective of this work is to explore the possible biases that individuals may have toward the perceived functionality of machine generated designs, compared to human created designs. Toward this end, 1187 participants were recruited via Amazon mechanical Turk (AMT) to analyze the perceived functional characteristics of both human created two-dimensional (2D) sketches and sketches generated by a deep learning generative model. In addition, a computer simulation was used to test the capability of the sketched ideas to perform their intended function and explore the validity of participants' responses. The results reveal that both participants and computer simulation evaluations were in agreement, indicating that sketches generated via the deep generative design model were more likely to perform their intended function, compared to human created sketches used to train the model. The results also reveal that participants were subject to biases while evaluating the sketches, and their age and domain knowledge were positively correlated with their perceived functionality of sketches. The results provide evidence that supports the capabilities of deep learning generative design tools to generate functional ideas and their potential to assist designers in creative tasks such as ideation.

scholarly journals Controllable inverse design of auxetic metamaterials using deep learning

2021 ◽  
pp. 110178
Author(s):  
Xiaoyang Zheng ◽  
Ta-Te Chen ◽  
Xiaofeng Guo ◽  
Sadaki Samitsu ◽  
Ikumu Watanabe
Keyword(s):  
Deep Learning ◽  
Inverse Design

scholarly journals Generative deep learning as a tool for inverse design of high entropy refractory alloys

2021 ◽  
Author(s):  
Arindam Debnath ◽  
Adam M. Krajewski ◽  
Hui Sun ◽  
Shuang Lin ◽  
Marcia Ahn ◽  
...  
Keyword(s):  
Deep Learning ◽  
Inverse Design ◽  
High Entropy ◽  
Refractory Alloys

scholarly journals Beyond Generative Models: Superfast Traversal, Optimization, Novelty, Exploration and Discovery (STONED) Algorithm for Molecules using SELFIES

2021 ◽  
Author(s):  
AkshatKumar Nigam ◽  
Robert Pollice ◽  
Mario Krenn ◽  
Gabriel dos Passos Gomes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Deep Learning ◽  
Virtual Screening ◽  
Chemical Space ◽  
Generative Models ◽  
Inverse Design ◽  
Learning Models ◽  
Structure Modification ◽  
Design Models ◽  
Comparable Performance ◽  
And Training

Inverse design allows the design of molecules with desirable properties using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED – a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. We achieve comparable performance on typical benchmarks without any training. We demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. We anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wide adoption.

scholarly journals Rapid Prototype Design of the Kinetic Sculpture Based on Sketches and Skeletons

2020 ◽  
pp. 21-26
Author(s):  
Xinwei Zhang ◽  
◽  
Jin Wang ◽  
Jinsong Xiao ◽  
Guodong Lu ◽  
...  
Keyword(s):  
Design Process ◽  
Installation Art ◽  
Rapid Prototype ◽  
Urban Landscapes ◽  
Design Tools ◽  
Motion Simulation ◽  
Visual Effects ◽  
Engineering Knowledge ◽  
Prototype Design ◽  
Generation Unit

The kinetic sculpture is an important aspect of modern urban landscapes and installation art. However, designing such a sculpture needs much time, imagination, engineering knowledge and even rework, which restrains iterations of design and imagination of artists. To allow designers to have a holistic perspective in design, and to simplify and accelerate the design process, we proposed a system to assist designers to build kinetic sculpture digital prototypes from sketches rapidly. The system simplifies the design pipeline as sketch processing, skeleton generation, unit assignment, and motion simulation. Different from other design tools, our system liberates designers from detailed modeling and animation. Therefore, they can focus on perfecting visual effects. User cases show that our system can spark the creativity and accelerate design process both for professional and inexperienced designers, and can generalize the pipeline to other kinetic applications.

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