WaiChing Sun

About WaiChing Sun

WaiChing Sun, With an exceptional h-index of 39 and a recent h-index of 32 (since 2020), a distinguished researcher at Columbia University in the City of New York, specializes in the field of Computational Mechanics, Machine Learning in Mechanics, Poromechanics, Geomechanics, Rock Physics.

His recent articles reflect a diverse array of research interests and contributions to the field:

Physics-Informed Diffusion Models

Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions

A machine‐learning supported multi‐scale LBM‐TPM model of unsaturated, anisotropic, and deformable porous materials

Prediction of effective elastic moduli of rocks using Graph Neural Networks

Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria

Prediction of yield surface of single crystal copper from discrete dislocation dynamics and geometric learning

Viscoelasticty with physics-augmented neural networks: Model formulation and training methods without prescribed internal variables

Domain partitioning material point method for simulating shock in polycrystalline energetic materials

WaiChing Sun Information

University

Position

Associate Professor of Civil Engineering and Engineering Mechanics

Citations(all)

4083

Citations(since 2020)

3181

Cited By

2166

hIndex(all)

39

hIndex(since 2020)

32

i10Index(all)

74

i10Index(since 2020)

66

Email

University Profile Page

Google Scholar

WaiChing Sun Skills & Research Interests

Computational Mechanics

Machine Learning in Mechanics

Poromechanics

Geomechanics

Rock Physics

Top articles of WaiChing Sun

Physics-Informed Diffusion Models

arXiv preprint arXiv:2403.14404

2024/3/21

Waiching Sun
Waiching Sun

H-Index: 28

Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions

Computer Methods in Applied Mechanics and Engineering

2024/3/15

A machine‐learning supported multi‐scale LBM‐TPM model of unsaturated, anisotropic, and deformable porous materials

International Journal for Numerical and Analytical Methods in Geomechanics

2024/3

Yousef Heider
Yousef Heider

H-Index: 11

Waiching Sun
Waiching Sun

H-Index: 28

Prediction of effective elastic moduli of rocks using Graph Neural Networks

Computer Methods in Applied Mechanics and Engineering

2024/3/1

Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria

Computer Methods in Applied Mechanics and Engineering

2024/3/1

Waiching Sun
Waiching Sun

H-Index: 28

Markus Kästner
Markus Kästner

H-Index: 19

Prediction of yield surface of single crystal copper from discrete dislocation dynamics and geometric learning

Journal of the Mechanics and Physics of Solids

2024/2/17

Waiching Sun
Waiching Sun

H-Index: 28

Wei Cai
Wei Cai

H-Index: 0

Viscoelasticty with physics-augmented neural networks: Model formulation and training methods without prescribed internal variables

arXiv preprint arXiv:2401.14270

2024/1/25

Waiching Sun
Waiching Sun

H-Index: 28

Markus Kästner
Markus Kästner

H-Index: 19

Domain partitioning material point method for simulating shock in polycrystalline energetic materials

Computer Methods in Applied Method and Engineering

2023

Ran Ma
Ran Ma

H-Index: 6

Waiching Sun
Waiching Sun

H-Index: 28

Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph

International Journal for Multiscale Computational Engineering

2023

A neural kernel method for capturing multiscale high-dimensional micromorphic plasticity of materials with internal structures

Computer Methods in Applied Mechanics and Engineering

2023/11/1

Waiching Sun
Waiching Sun

H-Index: 28

Physics-constrained symbolic model discovery for polyconvex incompressible hyperelastic materials

arXiv preprint arXiv:2310.04286

2023/10/6

Bahador Bahmani
Bahador Bahmani

H-Index: 5

Waiching Sun
Waiching Sun

H-Index: 28

Geometric learning for computational mechanics, Part III: Physics-constrained response surface of geometrically nonlinear shells

Computer Methods in Applied Mechanics and Engineering

2023/10/1

Ran Ma
Ran Ma

H-Index: 6

Waiching Sun
Waiching Sun

H-Index: 28

A publicly available PyTorch-ABAQUS UMAT deep-learning framework for level-set plasticity

Mechanics of Materials

2023/9/1

Hyoung Suk Suh
Hyoung Suk Suh

H-Index: 6

Waiching Sun
Waiching Sun

H-Index: 28

Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties

Computer Methods in Applied Mechanics and Engineering

2023/8/1

Waiching Sun
Waiching Sun

H-Index: 28

Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced kalman filter

Computational Mechanics

2023/7

Waiching Sun
Waiching Sun

H-Index: 28

Synthesizing realistic sand assemblies with denoising diffusion in latent space

arXiv preprint arXiv:2306.04411

2023/6/7

Waiching Sun
Waiching Sun

H-Index: 28

Objectivity and accuracy enhancement within ANN‐based multiscale material modeling

PAMM

2023/3

Yousef Heider
Yousef Heider

H-Index: 11

Waiching Sun
Waiching Sun

H-Index: 28

Distance-preserving manifold denoising for data-driven mechanics

Computer Methods in Applied Mechanics and Engineering

2023/2/15

Bahador Bahmani
Bahador Bahmani

H-Index: 5

Waiching Sun
Waiching Sun

H-Index: 28

Homogenized data magneto-active polymers

2023

Waiching Sun
Waiching Sun

H-Index: 28

Markus Kästner
Markus Kästner

H-Index: 19

Geometric prior of multi-resolution yielding manifolds and the local closest point projection for nearly non-smooth plasticity

Computer Methods in Applied Mechanics and Engineering

2022/10/1

Waiching Sun
Waiching Sun

H-Index: 28

See List of Professors in WaiChing Sun University(Columbia University in the City of New York)

Co-Authors

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