Shape Structuralizer

Chidambaram, Subramanian; Zhang, Yunbo; Sundararajan, Venkatraghavan; Elmqvist, Niklas; Ramani, Karthik

doi:10.1145/3290605.3300893

Cited by 12 publications

References 30 publications

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Machine learning driven interpretation of computational fluid dynamics simulations to develop student intuition

Patterson

2020

Comp Applic In Engineering

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Employers need engineers capable of leveraging CFD simulations to make intelligent design decisions, but undergraduate computational fluid dynamics (CFD) courses are not adequately preparing students for this type of work. CFD courses commonly familiarize students with topics, such as method derivation, domain creation, boundary conditions, mesh convergence, turbulence models, numerical convergence, and error analysis. This approach is an effective way to teach novices how CFD software works and how to prepare CFD analyses. However, it neglects development of higher level CFD skills and intuition important to engineering analysis and design, deferring this task to future study and training. This paper introduces the “Machine Learning Driven Interpretation of Fluid Dynamics Simulations to Develop Student Intuition” (MIFoS) software, a program designed to help CFD novices develop the high‐level skills and intuition that employers need in their engineers. A data‐driven approach was used to create the MIFoS software, which allows the submission of arbitrary geometries, automates an external flow simulation, and returns expert‐level graphical interpretation of simulation data. MIFoS's automated CFD simulation and feedback space allows novices to experiment with expert‐level suggestions on their own designs, enabling the skill and intuition development typically gained through years of study, practice, and expert guidance.

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Machine learning driven interpretation of computational fluid dynamics simulations to develop student intuition

Patterson

2020

Comp Applic In Engineering

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NormalF-Net: Normal Filtering Neural Network for Feature-preserving Mesh Denoising

Zhang

Feng

et al. 2020

Computer-Aided Design

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RodSteward: A Design‐to‐Assembly System for Fabrication using 3D‐Printed Joints and Precision‐Cut Rods

Jacobson

2019

Computer Graphics Forum

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We present RodSteward, a design‐to‐assembly system for creating furniture‐scale structures composed of 3D‐printed joints and precision‐cut rods. The RodSteward systems consists of: RSDesigner, a fabrication‐aware design interface that visualizes accurate geometries during edits and identifies infeasible designs; physical fabrication of parts automatically generated 3D‐printable joint geometries and cutting plans for rods; and RSAssembler, a guided‐assembly interface that prompts the user to place parts in order while showing a focus+context visualization of the assembly in progress. We demonstrate the effectiveness of our tools with a number of example constructions of varying complexity, style and parameter choices.

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Shape Structuralizer

Cited by 12 publications

References 30 publications

Machine learning driven interpretation of computational fluid dynamics simulations to develop student intuition

Machine learning driven interpretation of computational fluid dynamics simulations to develop student intuition

NormalF-Net: Normal Filtering Neural Network for Feature-preserving Mesh Denoising

RodSteward: A Design‐to‐Assembly System for Fabrication using 3D‐Printed Joints and Precision‐Cut Rods

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