TALK

Conference Talk

12th International Conference on IsoGeometric Analysis (IGA 2024)

Abstract Presentation:

Machine Learning and IGA: "Automatic Generation of NURBS-Based Geometries For IsoGeometric Analysis" by Kevin Wang (October 2024, Augustine, Florida, USA)

Abstract: Isogeometric analysis (IGA) combines computer-aided design (CAD) and finite element analysis (FEA) using Non-Uniform Rational B-Splines (NURBS) for both geometric representation and solution approximation. Despite its benefits like enhanced accuracy, IGA faces challenges in accurately modeling complex geometries and creating corresponding high-quality meshes. This paper introduces an advanced analytical framework designed to streamline and theoretically support the generation of curvilinear configurations. This includes a refined interpolation method for adjusting the coordinates of initial results, enhancements in NURBS surface and knot vector refinement, and a more robust material analytical solution in IGA. This framework effectively closes the gap between generated data and its physical representation. The notable advancements of this approach include the integration of curve generation and a comprehensive framework for a more precise and extensive representation of meshes and material properties.

ASME IDETC-CIE 2024

Technique Research Paper :

"Enhancing Isogeometric Analysis with NURBS-Based Synthesis" by Shaoliang Yang and Kevin Wang (August 2024, DC, USA)

Abstract: Isogeometric analysis (IGA) is a computational technique that integrates computer-aided design (CAD) with finite element analysis (FEA) by employing the same basis functions for both geometry representation and solution approximation. While IGA offers numerous advantages, such as improved accuracy and efficiency, it also presents several challenges related to geometric modeling. Some of these challenges include accurately representing complex geometries with NURBS (Non-Uniform Rational B-Splines) or other basis functions used in IGA and generating high-quality meshes that conform to the complex geometry represented by NURBS curves/surfaces. This paper introduces an analytical framework to provide a more efficient and theoretically grounded method for generating curvilinear configurations and its analytical solution in IGA, bridging the gap between generated data and its physical representations. This innovative approach is distinguished by integrating the NURBS parameterization in curve generation and providing a corresponding framework to achieve a broader and more accurate explanation of meshes and properties, especially constructing new coordinates and calculating the physical displacements under these conditions. Our model enables the analytical understanding of complex curves from the UIUC airfoil and superformula datasets, demonstrating a deeper dive into simulations. This study signifies a pivotal juncture wherein machine-learning-based complex geometrical formulations are synergistically combined with actual isogeometric analysis.

Invited Talk

Aerospace Corporation

Guest Speaker:

"Computational and Machine Learning-Driven Next-Generation Engineering Design Tools: Towards Advanced Manufacturing" by Dr. Uchechukwu Agwu (Summer 2025)

SRI International

Guest Speaker:

"Computational and Machine Learning-Driven Next-Generation Engineering Design Tools: Towards Advanced Manufacturing" by Dr. Morad Behandish (Spring 2025)

Santa Clara University

Guest Speaker:

MECH speaker series "Next-Generation Engineering Design Tools for Advanced Manufacturing" by Dr. Hohyun Lee (Winter 2023)

Huazhong University of Science and Technology

Guest Speaker:

"Computational and Machine Learning-Driven Methods for Next-Generation Engineering Design Tools" by Dr. Liang Xia (Fall 2022)

University of California, Merced

Guest Speaker:

SPARK seminar "Engineering Inspired by Nature" by Dr. Sachin Goyal (Spring 2021)

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