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WaiChing Sun on Muck Rack

WaiChing Sun

New York
As seen in: Wiley Online Library
Professor of Civil Engineering at Columbia University. Former Sandia Research Scientist. Editor of IJNME. AI for physics and mechanics of materials.
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Articles

Mixing Data‐Driven and Physics‐Based Constitutive Models Using Uncertainty‐Driven Phase Fields

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By Geosciences Delft, WaiChing Sun, Joep Storm
| Wiley Online Library Verified
1 Introduction Advanced manufacturing techniques allow for the creation of materials with properties tailored to their use. Multiscale modeling can accurately capture the behavior of these materials, yet its use is limited by the high computational cost of simulations. The need for more computationally efficient models, combined with the progress in machine learning, has resulted in numerous data-driven models [1-6].
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Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functions

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By Karl A. Kalina, Jörg Brummund, WaiChing Sun, Markus Kastner
| tu-dresden.de
Abstract We present a data-driven framework for the multiscale modeling of anisotropic finite strain elasticity based on physics-augmented neural networks (PANNs). Our approach allows the efficient simulation of materials with complex underlying microstructures which reveal an overall anisotropic and nonlinear behavior on the macroscale.
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Synthesizing realistic sand assemblies with denoising diffusion in latent space

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By WaiChing Sun, Khalid A. Alshibli, Richard A. Regueiro, Nikolaos N. Vlassis
| Wiley Online Library Verified
1 INTRODUCTION Sand is the second most consumed natural resource material in the world1 (the first being water). Every year, 50 billion tonnes of sand are used as raw material for concrete, glass, and electronics.
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