Waiching Sun

Waiching Sun
Columbia University | CU · Department of Civil Engineering and Engineering Mechanics

Theoretical and Applied Mechanics, Northwestern University, PhD

Publications

Publications (129)
Article
Full-text available
Many geological materials, such as shale, mudstone, carbonate rock, limestone and rock salt are multi-porosity porous media in which pores of different scales may co-exist in the host matrix. When fractures propagate in these multi-porosity materials, these pores may enlarge and coalesce and therefore change the magnitude and the principal directio...
Article
Full-text available
This paper presents a new meta-modeling framework to employ deep reinforcement learning (DRL) to generate mechanical constitutive models for interfaces. The constitutive models are conceptualized as information flow in directed graphs. The process of writing constitutive models is simplified 8 as a sequence of forming graph edges with the goal of m...
Article
Full-text available
This article introduces a manifold embedding data-driven paradigm to solve small-and finite-strain elasticity problems without a conventional constitutive law. This formulation follows the classical 6 data-driven paradigm by seeking the solution that obeys the balance of linear momentum and compatibility conditions, while remaining consistent with...
Article
Full-text available
This paper introduces an explicit material point method designed specifically for simulating the micropolar continuum dynamics in the finite deformation and finite microrotation regime. The material point method enables us to simulate large deformation problems while circumventing the potential mesh distortion without remeshing. To eliminate rotati...
Article
Full-text available
We introduce a deep learning framework designed to train smoothed elastoplasticity models with interpretable components, such as stored elastic energy function, field surface, and plastic flow that may evolve based on a set of deep neural network predictions. By recasting the yield function as an evolving level set, we introduce a deep learning app...
Article
Full-text available
For material modeling and discovery, synthetic microstructures play a critical role as digital twins. They provide stochastic samples upon which direct numerical simulations can be conducted to populate material databases. A large ensemble of simulation data on synthetic microstructures may provide supplemental data to inform and refine macroscopic...
Article
Full-text available
This article presents a multi-phase-field poromechanics model that simulates the growth and thaw of ice lenses and the resultant frozen heave and thaw settlement in multi-constituent frozen soils. In this model, the growth of segregated ice inside the freezing-induced fracture is implicitly represented by the evolution of two phase fields that indi...
Article
Full-text available
This paper presents a combined experimental-modeling effort to interpret the coupled thermo-hydro-mechanical behaviors of the freezing soil, where an unconfined, fully saturated clay is frozen due to a temperature gradient. By leveraging the rich experimental data from the microCT images and the measurements taken during the freezing process, we ex...
Thesis
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Global challenges associated with extreme climate events and increasing energy demand require significant advances in our understanding and predictive capability of coupled multi- physical processes across spatial and temporal scales. While classical approaches based on the mixture theory may shed light on the macroscopic poromechanics simulations,...
Article
Full-text available
We present a machine learning framework to train and validate neural networks to predict the anisotropic elastic response of a monoclinic organic molecular crystal known as Octogen (β-HMX) in the geometrical nonlinear regime. A filtered molecular dynamic (MD) simulations database is used to train neural networks with a Sobolev norm that uses the st...
Article
Full-text available
This paper presents a computational framework that generates ensemble predictive mechanics models with uncertainty quantification (UQ). We first develop a causal discovery algorithm to infer causal relations among time-history data measured during each representative volume element (RVE) simulation through a directed acyclic graph. With multiple pl...
Preprint
Full-text available
This article introduces a new data-driven approach that leverages a manifold embedding generated by the invertible neural network to improve the robustness, efficiency, and accuracy of the constitutive-law-free simulations with limited data. We achieve this by training a deep neural network to globally map data from the constitutive manifold onto a...
Article
Full-text available
This paper presents the mathematical framework and the asynchronous finite element solver that captures the brittle fractures in multi-phase fluid-infiltrating porous media at the mesoscale where the constituents are not necessarily in a thermal equilibrium state. To achieve this goal, we introduce a dual-temperature effective medium theory in whic...
Preprint
Full-text available
This article presents a multi-phase-field poromechanics model that simulates the growth and thaw of ice lenses and the resultant frozen heave and thaw settlement in multi-constituent frozen soils. In this model, the growth of segregated ice inside the freezing-induced fracture is implicitly represented by the evolution of two phase fields that indi...
Preprint
Full-text available
We present a machine learning framework to train and validate neural networks to predict the anisotropic elastic response of the monoclinic organic molecular crystal $\beta$-HMX in the geometrical nonlinear regime. A filtered molecular dynamic (MD) simulations database is used to train the neural networks with a Sobolev norm that uses the stress me...
Article
Full-text available
Conventionally, neural network constitutive laws for path-dependent elasto-plastic solids are trained via supervised learning performed on recurrent neural networks, with the time history of strain as input and the stress as input. However, training a neural network to replicate path-dependent constitutive responses require significantly more amoun...
Article
Full-text available
We propose a material point method (MPM) to model the evolving multi-body contacts due to crack growth and fragmentation of thermo-elastic bodies. By representing particle interface with an implicit function, we adopt the gradient partition techniques introduced by Homel and Herbold 2017 to identify the separation between a pair of distinct materia...
Preprint
Full-text available
This paper presents a PINN training framework that employs (1) pre-training steps that accelerates and improve the robustness of the training of physics-informed neural network with auxiliary data stored in point clouds, (2) a net-to-net knowledge transfer algorithm that improves the weight initialization of the neural network and (3) a multi-objec...
Article
Full-text available
We present a hybrid model/model-free data-driven approach to solve poroelasticity problems. Extending the data-driven modeling framework originated from \citet{kirchdoerfer2016data}, we introduce one model-free and two hybrid model-based/data-driven formulations capable of simulating the coupled diffusion-deformation of fluid-infiltrating porous me...

About

134
Publications
66,672
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Introduction
Sun’s research group specializes in the creation, derivation, implementation, verification, and validation of theoretical and computational models for engineering applications. The research group’s works include but not limited to the development of solution techniques for predicting brittle-ductile transition of porous media, formulations of stabilized mixed-field finite element model for large deformation multiphysics problems, data-driven modeling, digital rock and granular physics.
Additional affiliations
July 2020 - present
Columbia University
Position
  • Professor (Associate)
January 2014 - June 2020
Columbia University
Position
  • Professor (Assistant)
August 2013 - December 2013
Columbia University
Position
  • Research Assistant
Education
June 2008 - June 2011
Northwestern University
Field of study
  • Theoretical and Applied Mechanics
September 2007 - May 2008
Princeton University
Field of study
  • Civil Engineering
September 2005 - June 2007
Stanford University
Field of study
  • Geomechanics

Projects

Projects (7)
Project
It is our pleasure to invite you to contribute to the mini-symposium, MS23: Multiscale phase field and data-driven modeling of phase transition processes in porous media, which we are organizing at the InterPore2021, to take place online from 31 May to 3 June 2021. For further details please visit the web page: https://events.interpore.org/event/25/page/186-minisymposia Deadline for submission of abstracts is 08 February 2021. With best regards, Thomas Nagel - TU Bergakademie Freiberg, Germany WaiChing Sun - Columbia University, USA Yousef Heider - RWTH Aachen University, Germany ************************************************************************* (MS23) Multiscale phase field and data-driven modeling of phase transition processes in porous media Many mechanisms in multiphase porous materials under thermo-hydro-mechanical conditions can be classified as phase-change processes. The phase-field modeling approach can be used to model such coupled problems and has a range of attractive features as well as limitations in comparison to other schemes in porous media mechanics, both on the macro- and microscales. For this session, we particularly invite contributions on the following topics: * Freezing and thawing in saturated and unsaturated porous media: This includes experimental and numerical studies of soil and rock freezing, cyclic freezing/thawing, advanced constitutive models for frost damage, plasticity and creep, ice lens modeling, numerical algorithms and implementations, and stochastic methods in soil freezing. Solid-liquid phase-change processes in other materials, e.g. PCMs for heat storage, are likewise welcome. * Phase-field porous media fracture as a phase transition process: Crack propagation in porous media is widely treated within the phase-field method as a phase-change process between intact and fractured states of the material. A special focus is on the phase-field modeling of injection-induced and drying-induced fractures of porous media and the related thermo-hydro-mechanical changes. Both experimental and numerical studies on these topics are welcome. * Artificial intelligence approaches in phase transition processes: Multi-scale treatment of phase-change phenomena in porous media is not only complex but also computationally demanding. Techniques such as physics-guided neural networks, convolutional neural networks for predictions, AI image processing of microCT and SEM images, microstructure realization, reduced-order modeling, and others applied to the above applications are particularly welcome. * Additional topics: Related topics such as solid-phase transitions, microstructure evolution, and others will also be considered. *************************************************************************
Project
- Discrete Element Method (DEM) for micro-scale granular materials - Lattice Boltzmann Method (LBM) for multiphysics porous media - Micro to macro-scale homogenization approaches
Archived project
It is our pleasure to invite you to contribute to the mini-symposium, MS408: EXPERIMENTAL VALIDATION OF PHASE-FIELD MODELS FOR SOLID, FLUID AND POROUS MEDIA, which we are organizing at the 14th WCCM and ECCOMAS Congress 2020, to take place in Paris 19.-24. July 2020. This mini-symposium aims to provide a forum for presenting and discussing recent advances and challenges related to the experimental validation of PFM via comparison with real or lower-scale experimental results. This includes approaches related to the PF parameter estimation and numerical issues that affect the results quality. For further details please visit the web page: https://www.wccm-eccomas2020.org/admin/Files/FileAbstract/a408.pdf Deadline for submission of abstracts is December 15th 2019. With best regards, Yousef Heider (RWTH Aachen University, Germany) Fadi Aldakheel (Leibniz University Hannover, Germany) WaiChing Sun (Columbia University, USA) Thomas Wick (Leibniz University Hannover, Germany) SeonHong Na (McMaster University, Canada)